Mathematical Methods Handbook

Vector fields and their derivatives

Vector algebra, gradient, divergence and curl.

In this chapter 4

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  1. Vector algebra and differential operators

Vector algebra and differential operators

4

Definition

Vector algebra and index notation

Adapted

Gives the scalar, vector and triple products of vectors in R3\mathbb{R}^3 both geometrically and in components, introduces the summation convention with the Kronecker delta and the Levi-Civita symbol, and proves the ε\varepsilon-δ\delta identity that reduces every repeated cross product to dot products.

Definition(Scalar and vector products)AdaptedAdapted from Arfken, Weber & Harris (2013), §3.1, Properties 6–8 and Eq. (3.1), p. 124, and §3.2, Eqs. (3.2)–(3.9), pp. 126–128; Riley, Hobson & Bence (2006), §7.6.2, Eqs. (7.23)–(7.33), pp. 222–224

Throughout, a,b,c,d\mathbf{a}, \mathbf{b}, \mathbf{c}, \mathbf{d} are vectors of R3\mathbb{R}^3, written in the right-handed orthonormal basis e1,e2,e3\mathbf{e}_1, \mathbf{e}_2, \mathbf{e}_3 (also i,j,k\mathbf{i}, \mathbf{j}, \mathbf{k}, or e^x,e^y,e^z\hat{\mathbf{e}}_x, \hat{\mathbf{e}}_y, \hat{\mathbf{e}}_z) as a=a1e1+a2e2+a3e3\mathbf{a} = a_1\mathbf{e}_1 + a_2\mathbf{e}_2 + a_3\mathbf{e}_3. We write a=∣a∣=(a12+a22+a32)1/2a = |\mathbf{a}| = (a_1^2+a_2^2+a_3^2)^{1/2}, and θ∈[0,π]\theta \in [0,\pi] for the angle between two non-zero vectors placed tail to tail.

  • The scalar product (dot product) is a⋅b  =  a1b1+a2b2+a3b3  =  ∣a∣ ∣b∣cos⁡θ.\mathbf{a}\cdot\mathbf{b} \;=\; a_1b_1 + a_2b_2 + a_3b_3 \;=\; |\mathbf{a}|\,|\mathbf{b}|\cos\theta . It is symmetric and bilinear, ∣a∣2=a⋅a|\mathbf{a}|^2 = \mathbf{a}\cdot\mathbf{a}, and a⋅b=0\mathbf{a}\cdot\mathbf{b} = 0 for non-zero a,b\mathbf{a}, \mathbf{b} exactly when they are orthogonal. The component aia_i is the projection ei⋅a\mathbf{e}_i\cdot\mathbf{a}.
  • The vector product (cross product) a×b\mathbf{a}\times\mathbf{b} is the vector of length ∣a∣∣b∣sin⁡θ|\mathbf{a}||\mathbf{b}|\sin\theta — the area of the parallelogram with edges a\mathbf{a} and b\mathbf{b} — perpendicular to both, oriented so that a,b,a×b\mathbf{a}, \mathbf{b}, \mathbf{a}\times\mathbf{b} is a right-handed triple. In components, a×b  =  (a2b3−a3b2) e1+(a3b1−a1b3) e2+(a1b2−a2b1) e3  =  ∣e1e2e3a1a2a3b1b2b3∣,\mathbf{a}\times\mathbf{b} \;=\; (a_2b_3 - a_3b_2)\,\mathbf{e}_1 + (a_3b_1 - a_1b_3)\,\mathbf{e}_2 + (a_1b_2 - a_2b_1)\,\mathbf{e}_3 \;=\; \begin{vmatrix} \mathbf{e}_1 & \mathbf{e}_2 & \mathbf{e}_3 \\ a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \end{vmatrix}, the determinant being expanded along its first row. It is bilinear and anticommutative, b×a=− a×b\mathbf{b}\times\mathbf{a} = -\,\mathbf{a}\times\mathbf{b}, so a×a=0\mathbf{a}\times\mathbf{a} = \mathbf{0}; and a×b=0\mathbf{a}\times\mathbf{b} = \mathbf{0} for non-zero a,b\mathbf{a},\mathbf{b} exactly when they are parallel or antiparallel. It is not associative.
  • The scalar triple product is [a,b,c]=a⋅(b×c)[\mathbf{a},\mathbf{b},\mathbf{c}] = \mathbf{a}\cdot(\mathbf{b}\times\mathbf{c}), and the vector triple product is a×(b×c)\mathbf{a}\times(\mathbf{b}\times\mathbf{c}). In the second the brackets may not be dropped; in the first they may, since (a⋅b)×c(\mathbf{a}\cdot\mathbf{b})\times\mathbf{c} has no meaning.

The cross product is specific to three dimensions: the dot product and the identities below that involve no cross product hold in Rn\mathbb{R}^n for every nn.

Definition(Summation convention, Kronecker delta, Levi-Civita symbol)AdaptedAdapted from Arfken, Weber & Harris (2013), §2.1, Eq. (2.8), p. 85, and §3.2, Eqs. (3.5) and (3.8), p. 127; Riley, Hobson & Bence (2006), §26.1, p. 928

From here on, a lower-case Latin index repeated exactly twice in a term is summed over the values 1,2,31,2,3, and no summation sign is written: aibia_ib_i means ∑i=13aibi\sum_{i=1}^{3}a_ib_i, and aijbjka_{ij}b_{jk} means ∑j=13aijbjk\sum_{j=1}^{3}a_{ij}b_{jk}. A repeated index is a dummy index and may be renamed; an index occurring once is free, and the free indices on the two sides of an equation must match. No index may appear three times in a term.

The Kronecker delta is

δij={1,i=j,0,i≠j,\delta_{ij} = \begin{cases} 1, & i = j,\\ 0, & i \ne j,\end{cases}

so that ei⋅ej=δij\mathbf{e}_i\cdot\mathbf{e}_j = \delta_{ij}. Under the convention its effect is to rename an index: δijbj=bi\delta_{ij}b_j = b_i and δijajk=aik\delta_{ij}a_{jk} = a_{ik}. Note δii=3\delta_{ii} = 3.

The Levi-Civita symbol εijk\varepsilon_{ijk} is +1+1 when (i,j,k)(i,j,k) is an even permutation of (1,2,3)(1,2,3), namely 123123, 231231, 312312; it is −1-1 when (i,j,k)(i,j,k) is an odd permutation, namely 213213, 321321, 132132; and it is 00 whenever two of the three indices agree. Equivalently εijk\varepsilon_{ijk} is totally antisymmetric with ε123=1\varepsilon_{123} = 1: it changes sign when any two indices are interchanged, and is unchanged under a cyclic shift,

εijk=εjki=εkij=−εjik.\varepsilon_{ijk} = \varepsilon_{jki} = \varepsilon_{kij} = -\varepsilon_{jik}.
Definition(The products in index notation)AdaptedAdapted from Arfken, Weber & Harris (2013), §3.2, Eqs. (3.5), (3.8), p. 127 and Eqs. (3.12)–(3.13), pp. 128–129; Riley, Hobson & Bence (2006), §7.6.3, Eqs. (7.34)–(7.35), p. 225, and §26.8, Eq. (26.29), p. 942

With the conventions above,

a⋅b=aibi=δijaibj,ei×ej=εijk ek,(a×b)i=εijk ajbk,\mathbf{a}\cdot\mathbf{b} = a_ib_i = \delta_{ij}a_ib_j, \qquad \mathbf{e}_i\times\mathbf{e}_j = \varepsilon_{ijk}\,\mathbf{e}_k, \qquad (\mathbf{a}\times\mathbf{b})_i = \varepsilon_{ijk}\,a_jb_k,

and the scalar triple product is

a⋅(b×c)  =  εijk aibjck  =  ∣a1a2a3b1b2b3c1c2c3∣.\mathbf{a}\cdot(\mathbf{b}\times\mathbf{c}) \;=\; \varepsilon_{ijk}\,a_ib_jc_k \;=\; \begin{vmatrix} a_1 & a_2 & a_3 \\ b_1 & b_2 & b_3 \\ c_1 & c_2 & c_3\end{vmatrix} .

Because εijk\varepsilon_{ijk} is totally antisymmetric, the scalar triple product is unchanged by a cyclic permutation of a,b,c\mathbf{a}, \mathbf{b}, \mathbf{c} and changes sign under an interchange of two of them:

[a,b,c]=[b,c,a]=[c,a,b]=−[a,c,b]=−[b,a,c]=−[c,b,a].[\mathbf{a},\mathbf{b},\mathbf{c}] = [\mathbf{b},\mathbf{c},\mathbf{a}] = [\mathbf{c},\mathbf{a},\mathbf{b}] = -[\mathbf{a},\mathbf{c},\mathbf{b}] = -[\mathbf{b},\mathbf{a},\mathbf{c}] = -[\mathbf{c},\mathbf{b},\mathbf{a}] .

In particular a⋅(b×c)=(a×b)⋅c\mathbf{a}\cdot(\mathbf{b}\times\mathbf{c}) = (\mathbf{a}\times\mathbf{b})\cdot\mathbf{c}: the dot and the cross may be exchanged. Its absolute value is the volume of the parallelepiped with edges a,b,c\mathbf{a},\mathbf{b},\mathbf{c}, and it vanishes exactly when the three vectors are coplanar. More generally, for a 3×33\times 3 matrix AA,

εijk A1iA2jA3k=det⁡A.\varepsilon_{ijk}\,A_{1i}A_{2j}A_{3k} = \det A .
Definition(The ε\varepsilon-δ\delta identity and its contractions)AdaptedAdapted from Riley, Hobson & Bence (2006), §26.8, Eqs. (26.30), (26.35), (26.36), pp. 943–944

For all i,j,l,m∈{1,2,3}i,j,l,m \in \{1,2,3\},

εijk εklm  =  δilδjm−δimδjl(εδ)\varepsilon_{ijk}\,\varepsilon_{klm} \;=\; \delta_{il}\delta_{jm} - \delta_{im}\delta_{jl} \tag{$\varepsilon\delta$}

(the index kk summed). Contracting it once gives εijkεijm=2δkm\varepsilon_{ijk}\varepsilon_{ijm} = 2\delta_{km}, and contracting again gives εijkεijk=6\varepsilon_{ijk}\varepsilon_{ijk} = 6.

Idea of the proofOriginal

Both sides of (εδ)(\varepsilon\delta) vanish unless i≠ji \ne j and l≠ml \ne m. When those hold, the sum on the left has at most one non-zero term, because in three dimensions there is exactly one index outside the pair {i,j}\{i,j\} and exactly one outside {l,m}\{l,m\}; the term survives only if those two coincide, that is only if {i,j}={l,m}\{i,j\} = \{l,m\}. The remaining work is to check the sign in the two ways that can happen, and the antisymmetry of ε\varepsilon does that in one line each.

ProofAdaptedAdapted from Riley, Hobson & Bence (2006), §26.8, pp. 943–944

Write Lijlm=εijkεklmL_{ijlm} = \varepsilon_{ijk}\varepsilon_{klm} for the left side and Rijlm=δilδjm−δimδjlR_{ijlm} = \delta_{il}\delta_{jm} - \delta_{im}\delta_{jl} for the right side.

  1. Repeated indices in a pair. If i=ji = j then εijk=0\varepsilon_{ijk} = 0 for every kk, so Lijlm=0L_{ijlm} = 0; and Riilm=δilδim−δimδil=0R_{iilm} = \delta_{il}\delta_{im} - \delta_{im}\delta_{il} = 0. The same argument with the antisymmetry in the last two slots of εklm\varepsilon_{klm} disposes of l=ml = m. So assume from now on that i≠ji \ne j and l≠ml \ne m.

  2. At most one term survives. Since i≠ji \ne j, the factor εijk\varepsilon_{ijk} is non-zero for exactly one value of kk, namely the single element k0k_0 of {1,2,3}∖{i,j}\{1,2,3\}\setminus\{i,j\}; for the other two values of kk it has a repeated index. Likewise εklm\varepsilon_{klm} is non-zero only for k=k1k = k_1, the element of {1,2,3}∖{l,m}\{1,2,3\}\setminus\{l,m\}. Hence Lijlm=0L_{ijlm} = 0 unless k0=k1k_0 = k_1, which — the complements being singletons — happens exactly when {i,j}={l,m}\{i,j\} = \{l,m\}.

  3. The case i=li = l, j=mj = m. Then, using the cyclic invariance of ε\varepsilon in the second factor, εijkεkij=εijkεijk\varepsilon_{ijk}\varepsilon_{kij} = \varepsilon_{ijk}\varepsilon_{ijk} (no summation yet: only k=k0k = k_0 contributes), and a symbol with no repeated index squares to 11, so Lijij=1L_{ijij} = 1. On the right, δilδjm=1\delta_{il}\delta_{jm} = 1, while δimδjl=δijδji=0\delta_{im}\delta_{jl} = \delta_{ij}\delta_{ji} = 0 because i≠ji \ne j. So Rijij=1=LijijR_{ijij} = 1 = L_{ijij}.

  4. The case i=mi = m, j=lj = l. Now the second factor is εkji\varepsilon_{kji}, which is −εkij-\varepsilon_{kij} by antisymmetry in its last two indices, so by step 3 the surviving term is Lijji=−1L_{ijji} = -1. On the right, δilδjm=δijδji=0\delta_{il}\delta_{jm} = \delta_{ij}\delta_{ji} = 0 and δimδjl=1\delta_{im}\delta_{jl} = 1, so Rijji=−1=LijjiR_{ijji} = -1 = L_{ijji}.

  5. The remaining cases. If i≠ji \ne j, l≠ml \ne m and {i,j}≠{l,m}\{i,j\} \ne \{l,m\}, then Lijlm=0L_{ijlm} = 0 by step 2. For the right side, δilδjm≠0\delta_{il}\delta_{jm} \ne 0 forces i=li = l and j=mj = m, hence {i,j}={l,m}\{i,j\} = \{l,m\}, which is excluded; and δimδjl≠0\delta_{im}\delta_{jl} \ne 0 forces i=mi = m, j=lj = l, excluded for the same reason. So Rijlm=0R_{ijlm} = 0 as well. Steps 1–5 exhaust all index values, so (εδ)(\varepsilon\delta) holds.

  6. The contractions. Set l=il = i in (εδ)(\varepsilon\delta) and sum over ii as well as over kk. On the right, δiiδjm−δimδji=3δjm−δjm=2δjm\delta_{ii}\delta_{jm} - \delta_{im}\delta_{ji} = 3\delta_{jm} - \delta_{jm} = 2\delta_{jm}, by δii=3\delta_{ii} = 3 and the renaming rule for δ\delta. On the left we have εijkεkim\varepsilon_{ijk}\varepsilon_{kim}, which we bring to a symmetric shape in two steps: a cyclic shift gives εkim=εimk\varepsilon_{kim} = \varepsilon_{imk}, and then interchanging the last two indices of each factor, εijk=−εikj\varepsilon_{ijk} = -\varepsilon_{ikj} and εimk=−εikm\varepsilon_{imk} = -\varepsilon_{ikm}, costs two signs that cancel. So

    εijkεkim=εijkεimk=εikjεikm=2δjm,\varepsilon_{ijk}\varepsilon_{kim} = \varepsilon_{ijk}\varepsilon_{imk} = \varepsilon_{ikj}\varepsilon_{ikm} = 2\delta_{jm},

    in which the summed pair is now (i,k)(i,k) and the free pair is (j,m)(j,m). Renaming the dummies to i,ji,j and the free indices to k,mk,m gives εijkεijm=2δkm\varepsilon_{ijk}\varepsilon_{ijm} = 2\delta_{km}. Setting m=km = k in that and summing gives εijkεijk=2δkk=6\varepsilon_{ijk}\varepsilon_{ijk} = 2\delta_{kk} = 6. □\square

Definition(Triple-product identities)AdaptedAdapted from Riley, Hobson & Bence (2006), §7.6.3, Eq. (7.36) and §7.6.4, Eqs. (7.37)–(7.38), p. 226; Arfken, Weber & Harris (2013), §3.2, Eq. (3.18), p. 130

For all a,b,c,d∈R3\mathbf{a},\mathbf{b},\mathbf{c},\mathbf{d} \in \mathbb{R}^3:

  1. Vector triple product (the BAC–CAB rule) a×(b×c)=(a⋅c) b−(a⋅b) c,(a×b)×c=(a⋅c) b−(b⋅c) a.\mathbf{a}\times(\mathbf{b}\times\mathbf{c}) = (\mathbf{a}\cdot\mathbf{c})\,\mathbf{b} - (\mathbf{a}\cdot\mathbf{b})\,\mathbf{c}, \qquad (\mathbf{a}\times\mathbf{b})\times\mathbf{c} = (\mathbf{a}\cdot\mathbf{c})\,\mathbf{b} - (\mathbf{b}\cdot\mathbf{c})\,\mathbf{a}. In particular a×(b×c)\mathbf{a}\times(\mathbf{b}\times\mathbf{c}) lies in the plane of b\mathbf{b} and c\mathbf{c}, and the two bracketings differ in general.
  2. Lagrange's identity (a×b)⋅(c×d)=(a⋅c)(b⋅d)−(a⋅d)(b⋅c).(\mathbf{a}\times\mathbf{b})\cdot(\mathbf{c}\times\mathbf{d}) = (\mathbf{a}\cdot\mathbf{c})(\mathbf{b}\cdot\mathbf{d}) - (\mathbf{a}\cdot\mathbf{d})(\mathbf{b}\cdot\mathbf{c}). Taking c=a\mathbf{c} = \mathbf{a}, d=b\mathbf{d} = \mathbf{b} gives ∣a×b∣2=∣a∣2∣b∣2−(a⋅b)2|\mathbf{a}\times\mathbf{b}|^2 = |\mathbf{a}|^2|\mathbf{b}|^2 - (\mathbf{a}\cdot\mathbf{b})^2, which is the consistency of the geometric and component forms of the cross product, since 1−cos⁡2θ=sin⁡2θ1 - \cos^2\theta = \sin^2\theta.
Idea of the proofOriginal

Every one of these identities is a single application of (εδ)(\varepsilon\delta). Write the left-hand side in components; a repeated cross product produces two ε\varepsilon symbols sharing one dummy index; replace their product by the two δ\delta terms; each δ\delta then renames an index, and the two terms that come out are the two terms of the right-hand side.

ProofAdaptedAdapted from Arfken, Weber & Harris (2013), §3.2, Eq. (3.19), p. 130

1. Take the iith component of a×(b×c)\mathbf{a}\times(\mathbf{b}\times\mathbf{c}) and expand the inner product in components, choosing dummy indices that are not already in use:

[a×(b×c)]i=εijk aj (b×c)k=εijk εklm ajblcm.\bigl[\mathbf{a}\times(\mathbf{b}\times\mathbf{c})\bigr]_i = \varepsilon_{ijk}\,a_j\,(\mathbf{b}\times\mathbf{c})_k = \varepsilon_{ijk}\,\varepsilon_{klm}\,a_j b_l c_m .

The two symbols share the dummy index kk in the position required by (εδ)(\varepsilon\delta), so

[a×(b×c)]i=(δilδjm−δimδjl) ajblcm=ajbicj−ajbjci,\bigl[\mathbf{a}\times(\mathbf{b}\times\mathbf{c})\bigr]_i = (\delta_{il}\delta_{jm} - \delta_{im}\delta_{jl})\,a_j b_l c_m = a_j b_i c_j - a_j b_j c_i,

each δ\delta having renamed one index. The first term is (a⋅c)bi(\mathbf{a}\cdot\mathbf{c})b_i and the second is (a⋅b)ci(\mathbf{a}\cdot\mathbf{b})c_i, which is the first identity since ii was arbitrary. The second bracketing follows from the first: by anticommutativity (a×b)×c=− c×(a×b)(\mathbf{a}\times\mathbf{b})\times\mathbf{c} = -\,\mathbf{c}\times(\mathbf{a}\times\mathbf{b}), and the first identity applied to the right-hand side gives − (c⋅b)a+(c⋅a)b-\,(\mathbf{c}\cdot\mathbf{b})\mathbf{a} + (\mathbf{c}\cdot\mathbf{a})\mathbf{b}, as claimed.

2. For Lagrange's identity, expand both cross products and contract:

(a×b)⋅(c×d)=εkijaibj εklmcldm.(\mathbf{a}\times\mathbf{b})\cdot(\mathbf{c}\times\mathbf{d}) = \varepsilon_{kij}a_ib_j\,\varepsilon_{klm}c_ld_m .

A cyclic shift turns εkij\varepsilon_{kij} into εijk\varepsilon_{ijk}, so (εδ)(\varepsilon\delta) applies and gives

(a×b)⋅(c×d)=(δilδjm−δimδjl) aibjcldm=aici bjdj−amdm blcl,(\mathbf{a}\times\mathbf{b})\cdot(\mathbf{c}\times\mathbf{d}) = (\delta_{il}\delta_{jm} - \delta_{im}\delta_{jl})\,a_ib_jc_ld_m = a_ic_i\,b_jd_j - a_md_m\,b_lc_l,

which is (a⋅c)(b⋅d)−(a⋅d)(b⋅c)(\mathbf{a}\cdot\mathbf{c})(\mathbf{b}\cdot\mathbf{d}) - (\mathbf{a}\cdot\mathbf{d})(\mathbf{b}\cdot\mathbf{c}). □\square

RemarkOriginal

Where the hypotheses are used, and what the notation hides.

  • Three dimensions. Step 2 of the proof of (εδ)(\varepsilon\delta) uses that the complement of a two-element subset of the index set is a single element. That is false for n≠3n \ne 3, and indeed in Rn\mathbb{R}^n the totally antisymmetric symbol carries nn indices and (εδ)(\varepsilon\delta) is replaced by a determinant of δ\delta's. The scalar and dot-product identities, by contrast, need no restriction on nn.
  • Right-handedness. The formula (a×b)i=εijkajbk(\mathbf{a}\times\mathbf{b})_i = \varepsilon_{ijk}a_jb_k with ε123=+1\varepsilon_{123} = +1 presumes that e1,e2,e3\mathbf{e}_1,\mathbf{e}_2,\mathbf{e}_3 is right-handed. In a left-handed basis the same components describe − a×b-\,\mathbf{a}\times\mathbf{b}. Equivalently, a cross product reverses sign under a reflection relative to an ordinary vector; such objects are pseudovectors (see Pseudotensors and the Levi-Civita tensor), and the scalar triple product is a pseudoscalar.
  • Index discipline. The convention is only as safe as the bookkeeping. Introducing a dummy index that is already in use silently produces a wrong result, which is why in the proof above the inner cross product was expanded with the fresh indices l,ml, m rather than with j,kj, k again. Riley, Hobson and Bence make this point explicitly (p. 928): an index must never appear three times in a term.
  • Generality of the ε\varepsilon identity. Riley, Hobson and Bence also record the stronger statement that εijkεpqr\varepsilon_{ijk}\varepsilon_{pqr} equals the 3×33\times 3 determinant with entries δip,δiq,…,δkr\delta_{ip}, \delta_{iq}, \dots, \delta_{kr} (their Eq. (26.34), p. 944), and obtain (εδ)(\varepsilon\delta) from it by contraction. We have proved the contracted form directly, which is the only form the later units use.
  • What is not assumed. No completeness or orthonormality beyond ei⋅ej=δij\mathbf{e}_i\cdot\mathbf{e}_j = \delta_{ij} is used; in a non-orthonormal basis the component formulas above are all false, and one needs the metric tensor of The metric tensor.
ExampleOriginal

Take

a=(2,−1,3),b=(1,4,−2),c=(−3,2,1),d=(0,1,5).\mathbf{a} = (2,-1,3), \qquad \mathbf{b} = (1,4,-2), \qquad \mathbf{c} = (-3,2,1), \qquad \mathbf{d} = (0,1,5).

Step 1: the dot products. a⋅b=2−4−6=−8\mathbf{a}\cdot\mathbf{b} = 2 - 4 - 6 = -8, a⋅c=−6−2+3=−5\mathbf{a}\cdot\mathbf{c} = -6 - 2 + 3 = -5, b⋅c=−3+8−2=3\mathbf{b}\cdot\mathbf{c} = -3 + 8 - 2 = 3, a⋅d=0−1+15=14\mathbf{a}\cdot\mathbf{d} = 0 - 1 + 15 = 14, b⋅d=0+4−10=−6\mathbf{b}\cdot\mathbf{d} = 0 + 4 - 10 = -6.

Step 2: two cross products. From the component formula,

a×b=((−1)(−2)−3⋅4,  3⋅1−2⋅(−2),  2⋅4−(−1)⋅1)=(−10, 7, 9),\mathbf{a}\times\mathbf{b} = \bigl((-1)(-2) - 3\cdot 4,\; 3\cdot 1 - 2\cdot(-2),\; 2\cdot 4 - (-1)\cdot 1\bigr) = (-10,\,7,\,9),b×c=(4⋅1−(−2)⋅2,  (−2)(−3)−1⋅1,  1⋅2−4⋅(−3))=(8, 5, 14).\mathbf{b}\times\mathbf{c} = \bigl(4\cdot 1 - (-2)\cdot 2,\; (-2)(-3) - 1\cdot 1,\; 1\cdot 2 - 4\cdot(-3)\bigr) = (8,\,5,\,14).

As a check on orientation, a⋅(a×b)=−20−7+27=0\mathbf{a}\cdot(\mathbf{a}\times\mathbf{b}) = -20 - 7 + 27 = 0 and b⋅(a×b)=−10+28−18=0\mathbf{b}\cdot(\mathbf{a}\times\mathbf{b}) = -10 + 28 - 18 = 0.

Step 3: the scalar triple product two ways. Directly, a⋅(b×c)=16−5+42=53\mathbf{a}\cdot(\mathbf{b}\times\mathbf{c}) = 16 - 5 + 42 = 53. Exchanging dot and cross, (a×b)⋅c=30+14+9=53(\mathbf{a}\times\mathbf{b})\cdot\mathbf{c} = 30 + 14 + 9 = 53. The parallelepiped on a,b,c\mathbf{a},\mathbf{b},\mathbf{c} therefore has volume 5353, and the three vectors are not coplanar.

Step 4: the BAC–CAB rule. The right-hand side is

(a⋅c)b−(a⋅b)c=−5 (1,4,−2)+8 (−3,2,1)=(−5−24,  −20+16,  10+8)=(−29, −4, 18).(\mathbf{a}\cdot\mathbf{c})\mathbf{b} - (\mathbf{a}\cdot\mathbf{b})\mathbf{c} = -5\,(1,4,-2) + 8\,(-3,2,1) = (-5-24,\; -20+16,\; 10+8) = (-29,\,-4,\,18).

The left-hand side, computed from b×c=(8,5,14)\mathbf{b}\times\mathbf{c} = (8,5,14), is

a×(8,5,14)=((−1)(14)−3⋅5,  3⋅8−2⋅14,  2⋅5−(−1)⋅8)=(−29, −4, 18).\mathbf{a}\times(8,5,14) = \bigl((-1)(14) - 3\cdot 5,\; 3\cdot 8 - 2\cdot 14,\; 2\cdot 5 - (-1)\cdot 8\bigr) = (-29,\,-4,\,18).

The two agree. For contrast, (a×b)×c=(a⋅c)b−(b⋅c)a=−5 (1,4,−2)−3 (2,−1,3)=(−11,−17,1)(\mathbf{a}\times\mathbf{b})\times\mathbf{c} = (\mathbf{a}\cdot\mathbf{c})\mathbf{b} - (\mathbf{b}\cdot\mathbf{c})\mathbf{a} = -5\,(1,4,-2) - 3\,(2,-1,3) = (-11,-17,1), a different vector: the cross product is not associative.

Step 5: Lagrange's identity. Its right-hand side is (a⋅c)(b⋅d)−(a⋅d)(b⋅c)=(−5)(−6)−(14)(3)=30−42=−12(\mathbf{a}\cdot\mathbf{c})(\mathbf{b}\cdot\mathbf{d}) - (\mathbf{a}\cdot\mathbf{d})(\mathbf{b}\cdot\mathbf{c}) = (-5)(-6) - (14)(3) = 30 - 42 = -12. Its left-hand side needs c×d=(2⋅5−1⋅1,  1⋅0−(−3)⋅5,  −3−0)=(9,15,−3)\mathbf{c}\times\mathbf{d} = (2\cdot 5 - 1\cdot 1,\; 1\cdot 0 - (-3)\cdot 5,\; -3 - 0) = (9,15,-3), and then (a×b)⋅(c×d)=−90+105−27=−12(\mathbf{a}\times\mathbf{b})\cdot(\mathbf{c}\times\mathbf{d}) = -90 + 105 - 27 = -12. Finally, with c=a\mathbf{c} = \mathbf{a} and d=b\mathbf{d} = \mathbf{b} the identity reads ∣a×b∣2=100+49+81=230|\mathbf{a}\times\mathbf{b}|^2 = 100 + 49 + 81 = 230 against ∣a∣2∣b∣2−(a⋅b)2=14⋅21−64=294−64=230|\mathbf{a}|^2|\mathbf{b}|^2 - (\mathbf{a}\cdot\mathbf{b})^2 = 14\cdot 21 - 64 = 294 - 64 = 230, so the angle between a\mathbf{a} and b\mathbf{b} satisfies sin⁡2θ=230/294\sin^2\theta = 230/294 and cos⁡2θ=64/294\cos^2\theta = 64/294.

ApplicationOriginal

The moment of inertia tensor comes out of the BAC–CAB rule. A rigid body rotates about a fixed point with angular velocity ω\boldsymbol{\omega}, so the material point at position r\mathbf{r} moves with velocity v=ω×r\mathbf{v} = \boldsymbol{\omega}\times\mathbf{r}. Its contribution to the angular momentum about that point is r×(mv)=m r×(ω×r)\mathbf{r}\times(m\mathbf{v}) = m\,\mathbf{r}\times(\boldsymbol{\omega}\times\mathbf{r}), and the vector triple product with a=c=r\mathbf{a} = \mathbf{c} = \mathbf{r}, b=ω\mathbf{b} = \boldsymbol{\omega} turns this into

m r×(ω×r)=m(∣r∣2ω−(r⋅ω) r).m\,\mathbf{r}\times(\boldsymbol{\omega}\times\mathbf{r}) = m\bigl(|\mathbf{r}|^2\boldsymbol{\omega} - (\mathbf{r}\cdot\boldsymbol{\omega})\,\mathbf{r}\bigr).

Summing over the body, the iith component is Li=IijωjL_i = I_{ij}\omega_j with

Iij=∑αmα(∣rα∣2δij−xαixαj)I_{ij} = \sum_\alpha m_\alpha\bigl(|\mathbf{r}_\alpha|^2\delta_{ij} - x_{\alpha i}x_{\alpha j}\bigr)

(or the corresponding integral for a continuous body). Two things follow at once from the algebra and would be invisible in components: the angular momentum is in general not parallel to ω\boldsymbol{\omega}, because the second term subtracts the part of ω\boldsymbol{\omega} along r\mathbf{r}; and IijI_{ij} is symmetric, hence diagonalisable in an orthonormal basis (Spectral theorem for Hermitian matrices), which is what "principal axes" means. The same rule in the form r×(r×ω)\mathbf{r}\times(\mathbf{r}\times\boldsymbol{\omega}) reappears whenever a field is resolved into parts along and across a direction: −n^×(n^×A)=A−(n^⋅A)n^-\hat{\mathbf{n}}\times(\hat{\mathbf{n}}\times\mathbf{A}) = \mathbf{A} - (\hat{\mathbf{n}}\cdot\mathbf{A})\hat{\mathbf{n}} is the projection of A\mathbf{A} onto the plane perpendicular to the unit vector n^\hat{\mathbf{n}}, used for transverse electromagnetic field components and for the tangential part of a velocity field.

Lagrange's identity has a similarly practical use: for unit vectors n^1,n^2\hat{\mathbf{n}}_1, \hat{\mathbf{n}}_2 along two crystal axes, (n^1×n^2)⋅(n^1×n^2)=1−(n^1⋅n^2)2(\hat{\mathbf{n}}_1\times\hat{\mathbf{n}}_2)\cdot(\hat{\mathbf{n}}_1\times\hat{\mathbf{n}}_2) = 1 - (\hat{\mathbf{n}}_1\cdot\hat{\mathbf{n}}_2)^2 converts a measured angle between axes into the area of the cell face they span without computing a cross product at all. And the scalar triple product is the standard test for linear independence of three vectors, and the Jacobian determinant of a linear change of variables in R3\mathbb{R}^3 (Multiple integrals and change of variables).

Definition

Gradient and directional derivative

Adapted

Defines the operator ∇\nabla and the gradient of a scalar field, identifies ∇ϕ⋅a^\nabla\phi\cdot\hat{\mathbf{a}} as the rate of change of ϕ\phi per unit distance in the direction a^\hat{\mathbf{a}}, and shows that at a point where it does not vanish ∇ϕ\nabla\phi points along the steepest increase of ϕ\phi and normal to the level surface through that point.

Prerequisites: Partial derivatives and the chain rule, Implicit and inverse function theorems, Inner products and norms, Vector algebra and index notation

Definition(Scalar and vector fields; the operator ∇\nabla)AdaptedAdapted from Riley, Hobson & Bence (2006), §10.6, p. 347, and §10.7, Eq. (10.25), p. 348; Arfken, Weber & Harris (2013), §3.5, Eq. (3.44), p. 144

Let U⊆R3U \subseteq \mathbb{R}^3 be open. A scalar field on UU is a function ϕ:U→R\phi : U \to \mathbb{R}; a vector field on UU is a map F:U→R3\mathbf{F} : U \to \mathbb{R}^3, written F=F1e1+F2e2+F3e3\mathbf{F} = F_1\mathbf{e}_1 + F_2\mathbf{e}_2 + F_3\mathbf{e}_3 in the fixed right-handed orthonormal basis of Vector algebra and index notation. A field is of class CkC^k when its components are (Partial derivatives and the chain rule). The physical examples are the pressure or temperature in a fluid (scalar) and the fluid velocity or the electric field (vector).

The del operator (also nabla) is the formal vector of partial derivatives

∇  =  e1∂∂x1+e2∂∂x2+e3∂∂x3,in index notation(∇)i=∂i:=∂∂xi.\nabla \;=\; \mathbf{e}_1\frac{\partial}{\partial x_1} + \mathbf{e}_2\frac{\partial}{\partial x_2} + \mathbf{e}_3\frac{\partial}{\partial x_3}, \qquad\text{in index notation}\qquad (\nabla)_i = \partial_i := \frac{\partial}{\partial x_i}.

It is a vector differential operator: it acts on whatever stands to its right, so the order of symbols matters and brackets are not decoration. In particular ϕ∇\phi\nabla, whose components are ϕ ∂i\phi\,\partial_i, is again an operator and is a different object from the field ∇ϕ\nabla\phi defined next.

Definition(Gradient, directional derivative, normal derivative)AdaptedAdapted from Riley, Hobson & Bence (2006), §10.7.1, Eqs. (10.26), (10.30) and (10.32), pp. 348–350; Hirsch, Smale & Devaney (2013), §9.3, p. 202

Let ϕ\phi be a scalar field on an open U⊆R3U \subseteq \mathbb{R}^3 whose partial derivatives exist at a∈U\mathbf{a} \in U. The gradient of ϕ\phi at a\mathbf{a} is the vector

grad⁡ϕ  =  ∇ϕ  =  ∂ϕ∂x1e1+∂ϕ∂x2e2+∂ϕ∂x3e3,(∇ϕ)i=∂iϕ.\operatorname{grad}\phi \;=\; \nabla\phi \;=\; \frac{\partial\phi}{\partial x_1}\mathbf{e}_1 + \frac{\partial\phi}{\partial x_2}\mathbf{e}_2 + \frac{\partial\phi}{\partial x_3}\mathbf{e}_3, \qquad (\nabla\phi)_i = \partial_i\phi .

So ∇ϕ\nabla\phi is a vector field on UU whenever the partial derivatives exist throughout UU. The same formula with nn terms defines the gradient of a scalar field on an open subset of Rn\mathbb{R}^n.

Given a unit vector a^\hat{\mathbf{a}}, we call

dϕds  =  Da^ϕ(a)  =  ddt ϕ(a+ta^)∣t=0\frac{d\phi}{ds} \;=\; D_{\hat{\mathbf{a}}}\phi(\mathbf{a}) \;=\; \frac{d}{dt}\,\phi(\mathbf{a} + t\hat{\mathbf{a}})\Big|_{t=0}

the directional derivative along a^\hat{\mathbf{a}}: the rate of change of ϕ\phi per unit distance travelled in that direction; this is why a^\hat{\mathbf{a}} is normalised here, unlike the unnormalised DvfD_v f of Partial derivatives and the chain rule. A level surface (or level set) of ϕ\phi is a set ϕ−1(c)={x∈U:ϕ(x)=c}\phi^{-1}(c) = \{\mathbf{x} \in U : \phi(\mathbf{x}) = c\}, and a point of UU is a regular point of ϕ\phi if ∇ϕ≠0\nabla\phi \ne \mathbf{0} there. If n^\hat{\mathbf{n}} is the unit normal to a level surface pointing towards increasing ϕ\phi, the normal derivative is ∂ϕ/∂n=Dn^ϕ\partial\phi/\partial n = D_{\hat{\mathbf{n}}}\phi.

Definition(First-order change, directional derivative, steepest ascent)AdaptedAdapted from Arfken, Weber & Harris (2013), §3.5, Eqs. (3.41) and p. 145; Riley, Hobson & Bence (2006), §10.7.1, Eqs. (10.27)–(10.30), pp. 348–349; Hirsch, Smale & Devaney (2013), §9.3, p. 202

Let ϕ\phi be a scalar field on an open U⊆R3U \subseteq \mathbb{R}^3, differentiable at a∈U\mathbf{a} \in U in the sense of Partial derivatives and the chain rule.

  1. First-order change. ϕ(a+h)−ϕ(a)=∇ϕ(a)⋅h+o(∣h∣)\phi(\mathbf{a} + \mathbf{h}) - \phi(\mathbf{a}) = \nabla\phi(\mathbf{a})\cdot\mathbf{h} + o(|\mathbf{h}|) as h→0\mathbf{h}\to\mathbf{0}. Written with differentials, dϕ=∇ϕ⋅drd\phi = \nabla\phi\cdot d\mathbf{r}.
  2. Directional derivative. For every u∈R3\mathbf{u} \in \mathbb{R}^3 the derivative of ϕ\phi along u\mathbf{u} exists and equals ∇ϕ(a)⋅u\nabla\phi(\mathbf{a})\cdot\mathbf{u}; in particular dϕ/ds=∇ϕ(a)⋅a^d\phi/ds = \nabla\phi(\mathbf{a})\cdot\hat{\mathbf{a}} for a unit a^\hat{\mathbf{a}}. More generally, if r(u)\mathbf{r}(u) is a differentiable curve in UU then ddu ϕ(r(u))=∇ϕ(r(u))⋅drdu,\frac{d}{du}\,\phi(\mathbf{r}(u)) = \nabla\phi(\mathbf{r}(u))\cdot\frac{d\mathbf{r}}{du}, which for the arc-length parameter ss reads dϕ/ds=∇ϕ⋅t^d\phi/ds = \nabla\phi\cdot\hat{\mathbf{t}} with t^\hat{\mathbf{t}} the unit tangent.
  3. Steepest ascent. If ∇ϕ(a)≠0\nabla\phi(\mathbf{a}) \ne \mathbf{0}, then over all unit vectors a^\hat{\mathbf{a}} the number dϕ/ds=∇ϕ(a)⋅a^d\phi/ds = \nabla\phi(\mathbf{a})\cdot\hat{\mathbf{a}} is largest, and equal to ∣∇ϕ(a)∣|\nabla\phi(\mathbf{a})|, exactly for a^=∇ϕ(a)/∣∇ϕ(a)∣\hat{\mathbf{a}} = \nabla\phi(\mathbf{a})/|\nabla\phi(\mathbf{a})|; it is smallest, and equal to −∣∇ϕ(a)∣-|\nabla\phi(\mathbf{a})|, exactly for the opposite direction. So ∇ϕ\nabla\phi points along the steepest increase of ϕ\phi and its length is that steepest rate. If ∇ϕ(a)=0\nabla\phi(\mathbf{a}) = \mathbf{0} then dϕ/ds=0d\phi/ds = 0 in every direction.
Definition(The gradient is normal to the level surface)AdaptedAdapted from Hirsch, Smale & Devaney (2013), §9.3, pp. 203–204, Properties of Gradient Systems, part 1; Riley, Hobson & Bence (2006), §10.7.1, p. 350

Let ϕ\phi be a C1C^1 scalar field on an open U⊆R3U \subseteq \mathbb{R}^3 and let a∈U\mathbf{a} \in U be a regular point, ϕ(a)=c\phi(\mathbf{a}) = c.

  1. ∇ϕ(a)⋅v=0\nabla\phi(\mathbf{a})\cdot\mathbf{v} = 0 for every v\mathbf{v} that is tangent at a\mathbf{a} to the level surface ϕ−1(c)\phi^{-1}(c), in the sense that v=γ′(0)\mathbf{v} = \boldsymbol{\gamma}'(0) for some differentiable curve γ\boldsymbol{\gamma} with γ(0)=a\boldsymbol{\gamma}(0) = \mathbf{a} and ϕ∘γ\phi\circ\boldsymbol{\gamma} constant.
  2. Near a\mathbf{a}, ϕ−1(c)\phi^{-1}(c) is a surface: after renumbering coordinates so that ∂3ϕ(a)≠0\partial_3\phi(\mathbf{a}) \ne 0, there are a neighbourhood of a\mathbf{a} and a C1C^1 function gg of two variables such that the part of ϕ−1(c)\phi^{-1}(c) in that neighbourhood is the graph x3=g(x1,x2)x_3 = g(x_1,x_2). Its tangent vectors at a\mathbf{a} fill a plane, and ∇ϕ(a)\nabla\phi(\mathbf{a}) spans the line orthogonal to it.
  3. Consequently, with r0\mathbf{r}_0 the position vector of a\mathbf{a} and n0=∇ϕ(a)\mathbf{n}_0 = \nabla\phi(\mathbf{a}), the tangent plane to ϕ−1(c)\phi^{-1}(c) at a\mathbf{a} is {r:(r−r0)⋅n0=0}\{\mathbf{r} : (\mathbf{r}-\mathbf{r}_0)\cdot\mathbf{n}_0 = 0\} and the normal line is {r:(r−r0)×n0=0}\{\mathbf{r} : (\mathbf{r}-\mathbf{r}_0)\times\mathbf{n}_0 = \mathbf{0}\}. Writing n^=n0/∣n0∣\hat{\mathbf{n}} = \mathbf{n}_0/|\mathbf{n}_0|, we have ∇ϕ(a)=(∂ϕ/∂n) n^\nabla\phi(\mathbf{a}) = (\partial\phi/\partial n)\,\hat{\mathbf{n}} with ∂ϕ/∂n=∣∇ϕ(a)∣\partial\phi/\partial n = |\nabla\phi(\mathbf{a})|.
Idea of the proofOriginal

Everything here is the chain rule plus the Cauchy–Schwarz inequality. The chain rule applied to the straight line t↦a+tut \mapsto \mathbf{a} + t\mathbf{u} turns the gradient into a dot product, so all the directional information in ϕ\phi at first order is the single vector ∇ϕ\nabla\phi tested against directions. Cauchy–Schwarz then says that a dot product against a unit vector is largest when the unit vector is aligned, which is steepest ascent, and is zero when the unit vector is orthogonal. The level surface is where ϕ\phi does not change, so its tangent directions are exactly the directions in which the dot product vanishes — which is why the gradient leaves the surface at right angles.

ProofAdaptedAdapted from Hirsch, Smale & Devaney (2013), §9.3, pp. 202–204; Riley, Hobson & Bence (2006), §10.7.1, Eqs. (10.27)–(10.30), pp. 348–349

Part 1. Differentiability at a\mathbf{a} means ϕ(a+h)−ϕ(a)=Lh+o(∣h∣)\phi(\mathbf{a}+\mathbf{h}) - \phi(\mathbf{a}) = L\mathbf{h} + o(|\mathbf{h}|) for a linear form LL on R3\mathbb{R}^3 (Partial derivatives and the chain rule). Taking h=tei\mathbf{h} = t\mathbf{e}_i and dividing by tt shows Lei=∂iϕ(a)L\mathbf{e}_i = \partial_i\phi(\mathbf{a}), so Lh=∂iϕ(a)hi=∇ϕ(a)⋅hL\mathbf{h} = \partial_i\phi(\mathbf{a})h_i = \nabla\phi(\mathbf{a})\cdot\mathbf{h} by the index form of the dot product. This is part 1, and it is the precise content of the physicists' dϕ=∇ϕ⋅drd\phi = \nabla\phi\cdot d\mathbf{r}.

Part 2. Fix u\mathbf{u} and put ψ(t)=ϕ(a+tu)\psi(t) = \phi(\mathbf{a}+t\mathbf{u}) for ∣t∣|t| small enough that a+tu∈U\mathbf{a}+t\mathbf{u} \in U. By the chain rule for a differentiable map composed with a differentiable curve, ψ\psi is differentiable at 00 with ψ′(0)=∇ϕ(a)⋅u\psi'(0) = \nabla\phi(\mathbf{a})\cdot\mathbf{u}; the hypothesis used is differentiability of ϕ\phi at a\mathbf{a}, not merely the existence of its partial derivatives. Replacing the line by a differentiable curve r(u)\mathbf{r}(u) and applying the same chain rule at each parameter value gives the curve formula, and the arc-length case is that formula with ∣dr/ds∣=1|d\mathbf{r}/ds| = 1, so that dr/ds=t^d\mathbf{r}/ds = \hat{\mathbf{t}}.

Part 3. Let g=∇ϕ(a)≠0\mathbf{g} = \nabla\phi(\mathbf{a}) \ne \mathbf{0} and let a^\hat{\mathbf{a}} be a unit vector. By part 2, dϕ/ds=g⋅a^d\phi/ds = \mathbf{g}\cdot\hat{\mathbf{a}}. By the Cauchy–Schwarz inequality on R3\mathbb{R}^3 (Inner products and norms),

−∣g∣  =  −∣g∣ ∣a^∣  ≤  g⋅a^  ≤  ∣g∣ ∣a^∣  =  ∣g∣,-|\mathbf{g}| \;=\; -|\mathbf{g}|\,|\hat{\mathbf{a}}| \;\le\; \mathbf{g}\cdot\hat{\mathbf{a}} \;\le\; |\mathbf{g}|\,|\hat{\mathbf{a}}| \;=\; |\mathbf{g}|,

with equality on either side only when a^\hat{\mathbf{a}} and g\mathbf{g} are linearly dependent, that is a^=± g/∣g∣\hat{\mathbf{a}} = \pm\,\mathbf{g}/|\mathbf{g}| since both are then unit vectors; the sign of g⋅a^\mathbf{g}\cdot\hat{\mathbf{a}} decides which. Both bounds are attained, so the maximum is ∣g∣|\mathbf{g}| at a^=g/∣g∣\hat{\mathbf{a}} = \mathbf{g}/|\mathbf{g}| and the minimum is −∣g∣-|\mathbf{g}| at −g/∣g∣-\mathbf{g}/|\mathbf{g}|. If instead g=0\mathbf{g} = \mathbf{0}, then g⋅a^=0\mathbf{g}\cdot\hat{\mathbf{a}} = 0 for all a^\hat{\mathbf{a}}.

Normality, part 1 of the second statement. Let γ\boldsymbol{\gamma} be a differentiable curve with γ(0)=a\boldsymbol{\gamma}(0) = \mathbf{a} lying in ϕ−1(c)\phi^{-1}(c), so ϕ(γ(t))=c\phi(\boldsymbol{\gamma}(t)) = c for all small tt. Differentiating the constant function t↦ϕ(γ(t))t \mapsto \phi(\boldsymbol{\gamma}(t)) at t=0t = 0 and using the curve form of part 2,

0=ddt ϕ(γ(t))∣t=0=∇ϕ(a)⋅γ′(0),0 = \frac{d}{dt}\,\phi(\boldsymbol{\gamma}(t))\Big|_{t=0} = \nabla\phi(\mathbf{a})\cdot\boldsymbol{\gamma}'(0),

so ∇ϕ(a)\nabla\phi(\mathbf{a}) is orthogonal to every such tangent vector. Note that this direction of the argument needs no regularity assumption at all: it holds even where ∇ϕ\nabla\phi vanishes, where it says nothing.

Normality, part 2. Since a\mathbf{a} is a regular point, some partial derivative is non-zero there; renumber the coordinates so that ∂3ϕ(a)≠0\partial_3\phi(\mathbf{a}) \ne 0. Then the C1C^1 function Φ(x1,x2;x3)=ϕ(x1,x2,x3)\Phi(x_1,x_2;x_3) = \phi(x_1,x_2,x_3) has invertible partial derivative in its last variable at a\mathbf{a}, so by the implicit function theorem (Implicit and inverse function theorems) there are a neighbourhood WW of a\mathbf{a} and a C1C^1 function gg with ϕ−1(c)∩W={(x1,x2,g(x1,x2))}\phi^{-1}(c)\cap W = \{(x_1,x_2,g(x_1,x_2))\}. For each v\mathbf{v} in the plane P={v:∇ϕ(a)⋅v=0}P = \{\mathbf{v} : \nabla\phi(\mathbf{a})\cdot\mathbf{v} = 0\} — a plane, because ∇ϕ(a)≠0\nabla\phi(\mathbf{a}) \ne \mathbf{0} makes the condition one non-trivial linear equation — the curve t↦(a1+tv1, a2+tv2, g(a1+tv1,a2+tv2))t \mapsto (a_1 + tv_1,\, a_2 + tv_2,\, g(a_1+tv_1, a_2+tv_2)) lies in ϕ−1(c)\phi^{-1}(c), passes through a\mathbf{a} at t=0t=0, and is differentiable; its velocity there is a vector of PP with first two components v1,v2v_1, v_2, and since a vector of PP is determined by its first two components (the coefficient of x3x_3 in the equation of PP being ∂3ϕ(a)≠0\partial_3\phi(\mathbf{a}) \ne 0), that velocity is v\mathbf{v} itself. So every vector of PP is tangent to the level surface, and by the previous paragraph no vector outside PP is. Hence the tangent vectors form exactly PP, whose orthogonal complement is the line spanned by ∇ϕ(a)\nabla\phi(\mathbf{a}).

Normality, part 3. A point r\mathbf{r} lies in the plane through r0\mathbf{r}_0 with normal n0\mathbf{n}_0 precisely when (r−r0)⋅n0=0(\mathbf{r}-\mathbf{r}_0)\cdot\mathbf{n}_0 = 0, and on the line through r0\mathbf{r}_0 in the direction n0\mathbf{n}_0 precisely when (r−r0)×n0=0(\mathbf{r}-\mathbf{r}_0)\times\mathbf{n}_0 = \mathbf{0}, since a cross product vanishes exactly for parallel vectors (Vector algebra and index notation). Finally ∇ϕ(a)\nabla\phi(\mathbf{a}) is a positive multiple of n^\hat{\mathbf{n}} by construction, and part 3 of the first statement identifies that multiple as ∣∇ϕ(a)∣=∂ϕ/∂n|\nabla\phi(\mathbf{a})| = \partial\phi/\partial n. □\square

RemarkOriginal

Where the hypotheses are used, and what fails without them.

  • Differentiability, not just partial derivatives. The formula ∇ϕ\nabla\phi needs only the three partial derivatives, but every statement above needs differentiability at the point. For ϕ(x1,x2,x3)=x1x2/(x12+x22)\phi(x_1,x_2,x_3) = x_1x_2/(x_1^2+x_2^2) off the x3x_3-axis and 00 on it, both ∂1ϕ\partial_1\phi and ∂2ϕ\partial_2\phi vanish at the origin, so ∇ϕ(0)=0\nabla\phi(\mathbf{0}) = \mathbf{0}; yet ϕ=12\phi = \tfrac12 along the line x1=x2x_1 = x_2, x3=0x_3=0, so the directional derivative along (1,1,0)/2(1,1,0)/\sqrt{2} does not exist and ϕ\phi is not even continuous at the origin. The counterexamples in Partial derivatives and the chain rule are of exactly this kind. Class C1C^1 is the usual sufficient condition.
  • Regularity for the level surface. Part 1 of the normality statement holds everywhere; parts 2 and 3 need ∇ϕ(a)≠0\nabla\phi(\mathbf{a}) \ne \mathbf{0}, and that is where the implicit function theorem is applied. Without it a level set need not be a surface and has no tangent plane: for ϕ=x12+x22−x32\phi = x_1^2 + x_2^2 - x_3^2, the origin is a critical point and ϕ−1(0)\phi^{-1}(0) is a double cone, which has no tangent plane at its vertex. For ϕ=x12+x22+x32\phi = x_1^2+x_2^2+x_3^2 the level set ϕ−1(0)\phi^{-1}(0) is a single point.
  • Steepest ascent is a statement about unit directions. Dropping the normalisation makes it vacuous, since ∇ϕ⋅u\nabla\phi\cdot\mathbf{u} is unbounded in u\mathbf{u}. This is the one place where our normalised dϕ/dsd\phi/ds differs from the unnormalised directional derivative of Partial derivatives and the chain rule, and the two differ by the factor ∣u∣|\mathbf{u}|.
  • The equality case needs Cauchy–Schwarz with its equality case, not merely the inequality: without it one would know the maximum is at most ∣∇ϕ∣|\nabla\phi| but not that the gradient direction is the only maximiser.
  • Radial fields. For ϕ(r)=f(r)\phi(\mathbf{r}) = f(r) with r=∣r∣r = |\mathbf{r}|, the chain rule gives ∇ϕ=f′(r) r^\nabla\phi = f'(r)\,\hat{\mathbf{r}}, in particular ∇r=r^\nabla r = \hat{\mathbf{r}} and ∇rn=nrn−1r^\nabla r^n = n r^{n-1}\hat{\mathbf{r}}, as in Riley, Hobson and Bence (p. 355) and Arfken, Weber and Harris (Examples 3.5.1 and 3.5.3, pp. 145–146). All of these fail at r=0r = 0, where rr is not differentiable; Arfken, Weber and Harris note this exclusion for the Coulomb case n=−1n=-1.
NoteAdaptedAdapted from Lee (2013), Ch. 13, pp. 342–343

The gradient depends on the inner product; the differential does not. The object defined by the partial derivatives alone is the linear form dϕ:h↦∂iϕ hid\phi : \mathbf{h}\mapsto \partial_i\phi\, h_i, which needs no notion of length or angle. Turning that form into a vector requires an inner product, and the gradient is the vector that represents it: ∇ϕ\nabla\phi is the unique vector with ∇ϕ⋅v=dϕ(v)\nabla\phi\cdot\mathbf{v} = d\phi(\mathbf{v}) for all v\mathbf{v}. Lee makes exactly this the definition on a Riemannian manifold, writing grad⁡f=(df)♯\operatorname{grad} f = (df)^\sharp for the vector field characterised by ⟨grad⁡f,X⟩g=Xf\langle\operatorname{grad} f, X\rangle_g = Xf, with the coordinate expression gij(∂f/∂xi) ∂/∂xjg^{ij}(\partial f/\partial x^i)\,\partial/\partial x^j. Our formula is the case gij=δijg_{ij} = \delta_{ij} of his. The practical consequences: the componentwise formula above is valid only in orthonormal Cartesian coordinates (for others see Orthogonal curvilinear coordinates), and in a problem with a non-Euclidean metric — a curved space, or just a rescaling of units between axes — "the direction of steepest increase" changes while dϕd\phi does not.

ExampleOriginal

Let

ϕ(x,y,z)=x3y−2y2z+z2andP=(2,1,−1).\phi(x,y,z) = x^3y - 2y^2z + z^2 \qquad\text{and}\qquad P = (2,1,-1).

Step 1: the gradient. Differentiating,

∇ϕ=(3x2y,  x3−4yz,  −2y2+2z),\nabla\phi = \bigl(3x^2y,\; x^3 - 4yz,\; -2y^2 + 2z\bigr),

so at PP, ∇ϕ(P)=(3⋅4⋅1,  8−4⋅1⋅(−1),  −2+2(−1))=(12, 12, −4)\nabla\phi(P) = (3\cdot 4\cdot 1,\; 8 - 4\cdot 1\cdot(-1),\; -2 + 2(-1)) = (12,\,12,\,-4). Its length is ∣∇ϕ(P)∣=144+144+16=304=419≈17.4356|\nabla\phi(P)| = \sqrt{144+144+16} = \sqrt{304} = 4\sqrt{19} \approx 17.4356. Since this is non-zero, PP is a regular point.

Step 2: a directional derivative. In the direction a=(2,−1,2)\mathbf{a} = (2,-1,2), which has ∣a∣=3|\mathbf{a}| = 3, the unit vector is a^=13(2,−1,2)\hat{\mathbf{a}} = \tfrac13(2,-1,2) and

dϕds=∇ϕ(P)⋅a^=13(24−12−8)=43.\frac{d\phi}{ds} = \nabla\phi(P)\cdot\hat{\mathbf{a}} = \tfrac13\bigl(24 - 12 - 8\bigr) = \tfrac43 .

So ϕ\phi increases at 4/34/3 per unit length in that direction. The steepest rate at PP is 419≈17.444\sqrt{19} \approx 17.44, attained in the direction (12,12,−4)/(419)=(3,3,−1)/19(12,12,-4)/(4\sqrt{19}) = (3,3,-1)/\sqrt{19}, and the steepest decrease is −419-4\sqrt{19} in the opposite direction. Note 4/34/3 is far below 4194\sqrt{19}: the angle θ\theta between a^\hat{\mathbf{a}} and ∇ϕ(P)\nabla\phi(P) has cos⁡θ=(4/3)/(419)=1/(319)≈0.0765\cos\theta = (4/3)/(4\sqrt{19}) = 1/(3\sqrt{19}) \approx 0.0765, so the chosen direction is nearly tangent to the level surface.

Step 3: the level surface and its tangent plane. ϕ(P)=8⋅1−2⋅1⋅(−1)+1=11\phi(P) = 8\cdot 1 - 2\cdot 1\cdot(-1) + 1 = 11, so PP lies on ϕ−1(11)\phi^{-1}(11). By part 3 of the normality statement the tangent plane there is

12(x−2)+12(y−1)−4(z+1)=0,that is3x+3y−z=10,12(x-2) + 12(y-1) - 4(z+1) = 0, \qquad\text{that is}\qquad 3x + 3y - z = 10 ,

after dividing by 44; substituting PP gives 6+3+1=106+3+1 = 10, as it must. The normal line is r(t)=(2,1,−1)+t (3,3,−1)\mathbf{r}(t) = (2,1,-1) + t\,(3,3,-1).

Step 4: a check on orthogonality. The direction v=(1,−1,0)\mathbf{v} = (1,-1,0) satisfies ∇ϕ(P)⋅v=12−12+0=0\nabla\phi(P)\cdot\mathbf{v} = 12 - 12 + 0 = 0, so it should be tangent to the level surface. Indeed, moving from PP along v\mathbf{v} by tt gives

ϕ(2+t, 1−t, −1)=(2+t)3(1−t)+2(1−t)2+1=11−4t2−5t3−t4,\phi(2+t,\,1-t,\,-1) = (2+t)^3(1-t) + 2(1-t)^2 + 1 = 11 - 4t^2 - 5t^3 - t^4 ,

which agrees with 1111 to first order in tt — the change is O(t2)O(t^2), with no linear term — exactly as orthogonality to the gradient predicts. (Expanding: (2+t)3(1−t)=(8+12t+6t2+t3)(1−t)=8+4t−6t2−5t3−t4(2+t)^3(1-t) = (8+12t+6t^2+t^3)(1-t) = 8 + 4t - 6t^2 - 5t^3 - t^4, and 2(1−t)2=2−4t+2t22(1-t)^2 = 2 - 4t + 2t^2; the two linear terms, +4t+4t and −4t-4t, cancel, and −6t2+2t2=−4t2-6t^2+2t^2 = -4t^2.)

ApplicationOriginal

Constitutive laws are gradients, and that is why flux crosses level surfaces at right angles. Fourier's law of heat conduction in an isotropic medium is q=−k ∇T\mathbf{q} = -k\,\nabla T with k>0k > 0 the conductivity, and Fick's law of diffusion is J=−D ∇c\mathbf{J} = -D\,\nabla c. Both say the same two things, and both are statements of the present unit: heat and matter flow in the direction of steepest decrease of temperature or concentration, with a rate set by ∣∇T∣|\nabla T| or ∣∇c∣|\nabla c|; and the flux is everywhere perpendicular to the isotherms T=constT = \text{const} or the level sets of cc. The second point is what makes a sketch of the level surfaces of TT a complete qualitative picture of the flow, and the normal derivative ∂T/∂n=∣∇T∣\partial T/\partial n = |\nabla T| the quantity that appears in a Neumann boundary condition −k ∂T/∂n=qin-k\,\partial T/\partial n = q_{\text{in}} prescribing the heat entering a surface. In mechanics the same structure appears as F=−∇V\mathbf{F} = -\nabla V for a conservative force: the force is normal to the equipotential surfaces and points downhill in VV.

Steepest descent as an algorithm, and gradient flow. Because −∇ϕ-\nabla\phi is the direction of fastest decrease, the simplest way to minimise a smooth ϕ\phi numerically is to step along it: xn+1=xn−η ∇ϕ(xn)\mathbf{x}_{n+1} = \mathbf{x}_n - \eta\,\nabla\phi(\mathbf{x}_n). Its continuous-time limit is the gradient system x˙=−∇V(x)\dot{\mathbf{x}} = -\nabla V(\mathbf{x}) of Hirsch, Smale and Devaney (§9.3, pp. 202–204), and the unit explains its behaviour completely: along a solution, the chain rule gives ddtV(x(t))=∇V⋅x˙=−∣∇V∣2≤0\frac{d}{dt}V(\mathbf{x}(t)) = \nabla V\cdot\dot{\mathbf{x}} = -|\nabla V|^2 \le 0, so VV decreases strictly except at the equilibria, which are precisely the critical points of VV; and the trajectories cross the level sets of VV orthogonally. That is both why gradient descent cannot cycle and why it stalls in a long narrow valley, where the steepest direction points across the valley rather than along it. The same picture, with VV a free energy, is the relaxational dynamics of a physical system towards equilibrium.

Definition

Divergence, curl and the Laplacian

Adapted

Defines ∇⋅F\nabla\cdot\mathbf{F}, ∇×F\nabla\times\mathbf{F} and ∇2ϕ\nabla^2\phi in Cartesian coordinates and proves that for a C1C^1 field the divergence at a point is the limit of outward flux per unit volume over shrinking cubes, while the component of the curl along a unit vector is the limit of circulation per unit area over shrinking squares normal to it.

Prerequisites: Gradient and directional derivative

Definition(Divergence, curl and the Laplacian)AdaptedAdapted from Riley, Hobson & Bence (2006), §10.7.2, Eqs. (10.33)–(10.34), p. 352, and §10.7.3, Eq. (10.35), p. 353; Arfken, Weber & Harris (2013), §3.5, Eqs. (3.52) and (3.58), pp. 146 and 149, and §3.6, Eq. (3.62), p. 154

Let U⊆R3U \subseteq \mathbb{R}^3 be open, let F=F1e1+F2e2+F3e3\mathbf{F} = F_1\mathbf{e}_1 + F_2\mathbf{e}_2 + F_3\mathbf{e}_3 be a vector field on UU whose partial derivatives exist, and let ϕ\phi be a scalar field on UU with second partial derivatives. In the Cartesian basis and the index notation of Vector algebra and index notation:

  • The divergence of F\mathbf{F} is the scalar field div⁡F  =  ∇⋅F  =  ∂F1∂x1+∂F2∂x2+∂F3∂x3  =  ∂iFi.\operatorname{div}\mathbf{F} \;=\; \nabla\cdot\mathbf{F} \;=\; \frac{\partial F_1}{\partial x_1} + \frac{\partial F_2}{\partial x_2} + \frac{\partial F_3}{\partial x_3} \;=\; \partial_iF_i . A field with ∇⋅F=0\nabla\cdot\mathbf{F} = 0 throughout UU is called solenoidal.
  • The curl of F\mathbf{F} is the vector field curl⁡F  =  ∇×F  =  ∣e1e2e3∂∂x1∂∂x2∂∂x3F1F2F3∣,(∇×F)i=εijk ∂jFk,\operatorname{curl}\mathbf{F} \;=\; \nabla\times\mathbf{F} \;=\; \begin{vmatrix} \mathbf{e}_1 & \mathbf{e}_2 & \mathbf{e}_3 \\[2pt] \dfrac{\partial}{\partial x_1} & \dfrac{\partial}{\partial x_2} & \dfrac{\partial}{\partial x_3} \\[4pt] F_1 & F_2 & F_3 \end{vmatrix}, \qquad (\nabla\times\mathbf{F})_i = \varepsilon_{ijk}\,\partial_jF_k , the determinant being expanded along its first row from the top down, so that each derivative in the second row acts on the component of F\mathbf{F} in the third row and on nothing else. Explicitly its first component is ∂2F3−∂3F2\partial_2F_3 - \partial_3F_2, and the others follow by cyclic permutation of (1,2,3)(1,2,3). A field with ∇×F=0\nabla\times\mathbf{F} = \mathbf{0} throughout UU is called irrotational.
  • The Laplacian of ϕ\phi is the divergence of its gradient, ∇2ϕ  =  ∇⋅∇ϕ  =  ∂2ϕ∂x12+∂2ϕ∂x22+∂2ϕ∂x32  =  ∂i∂iϕ  =  δij ∂i∂jϕ.\nabla^2\phi \;=\; \nabla\cdot\nabla\phi \;=\; \frac{\partial^2\phi}{\partial x_1^2} + \frac{\partial^2\phi}{\partial x_2^2} + \frac{\partial^2\phi}{\partial x_3^2} \;=\; \partial_i\partial_i\phi \;=\; \delta_{ij}\,\partial_i\partial_j\phi . It is also written Δϕ\Delta\phi. The operator ∇2=∂i∂i\nabla^2 = \partial_i\partial_i applied to each Cartesian component of a vector field gives the vector Laplacian, (∇2F)i=∇2Fi(\nabla^2\mathbf{F})_i = \nabla^2F_i; this componentwise formula is a Cartesian accident, and the coordinate-free definition is the one in Vector differential identities.

Together with the gradient, ∇⋅\nabla\cdot and ∇×\nabla\times are the only first-order differential operators that ∇\nabla produces: ∇\nabla needs a scalar field to its right, ∇⋅\nabla\cdot and ∇×\nabla\times need a vector field, so ∇(∇ϕ)\nabla(\nabla\phi), ∇⋅(∇⋅F)\nabla\cdot(\nabla\cdot\mathbf{F}), ∇×(∇⋅F)\nabla\times(\nabla\cdot\mathbf{F}) and ∇(∇×F)\nabla(\nabla\times\mathbf{F}) are meaningless. Which second-order combinations do make sense, and how they are related, is the subject of Vector differential identities.

Definition(Divergence is flux density, curl is circulation density)AdaptedAdapted from Arfken, Weber & Harris (2013), §3.5, pp. 147–148, Eq. (3.55), and pp. 150–151, Eq. (3.60)

Let F\mathbf{F} be a vector field of class C1C^1 on an open U⊆R3U \subseteq \mathbb{R}^3 and let a∈U\mathbf{a} \in U.

  1. Flux. For h>0h > 0 small enough that it lies in UU, let QhQ_h be the closed cube of side 2h2h centred at a\mathbf{a} with faces normal to the coordinate axes, and let Φ(h)  =  ∫∂QhF⋅n^  dA\Phi(h) \;=\; \int_{\partial Q_h} \mathbf{F}\cdot\hat{\mathbf{n}}\; dA be the total flux of F\mathbf{F} out of QhQ_h, the sum of the six face integrals with n^\hat{\mathbf{n}} the outward unit normal. Then lim⁡h→0+ Φ(h)8h3  =  (∇⋅F)(a),\lim_{h\to 0^+}\ \frac{\Phi(h)}{8h^3} \;=\; (\nabla\cdot\mathbf{F})(\mathbf{a}), the denominator being the volume of QhQ_h. So the divergence at a point is the outward flux per unit volume in the limit of a small volume around it: it is positive at a source, negative at a sink, and zero for a flow that conserves whatever it transports.
  2. Circulation. Let u^,v^,n^\hat{\mathbf{u}}, \hat{\mathbf{v}}, \hat{\mathbf{n}} be a right-handed orthonormal triple, so u^×v^=n^\hat{\mathbf{u}}\times\hat{\mathbf{v}} = \hat{\mathbf{n}}. For h>0h > 0 small let Sh={a+su^+tv^:∣s∣≤h, ∣t∣≤h}S_h = \{\mathbf{a} + s\hat{\mathbf{u}} + t\hat{\mathbf{v}} : |s| \le h,\ |t|\le h\} be the square of side 2h2h centred at a\mathbf{a} in the plane normal to n^\hat{\mathbf{n}}, and let C(h)  =  ∮∂ShF⋅dℓC(h) \;=\; \oint_{\partial S_h} \mathbf{F}\cdot d\boldsymbol{\ell} be the circulation of F\mathbf{F} round its boundary, traversed anticlockwise as seen from the side n^\hat{\mathbf{n}} points to. Then lim⁡h→0+ C(h)4h2  =  (∇×F)(a)⋅n^,\lim_{h\to 0^+}\ \frac{C(h)}{4h^2} \;=\; (\nabla\times\mathbf{F})(\mathbf{a})\cdot\hat{\mathbf{n}}, the denominator being the area of ShS_h. So the component of the curl along a direction is the circulation per unit area round a small loop normal to that direction; the curl itself is the vector whose component in each direction gives that circulation density, and it vanishes at a point exactly when all three of these densities do.
Idea of the proofOriginal

Both limits are the same one-line calculation done twice. On a face of the cube, flux is a component of F\mathbf{F} integrated over an area; pair each face with the opposite one, and what is left is a difference of that component across a gap of width 2h2h, which the mean value theorem converts into 2h2h times a derivative. Three pairs of faces give the three terms of the divergence. For the loop, pair each side with the one opposite; the same difference appears, but now the two surviving derivatives are of different components in different directions, and they enter with opposite signs because the two sides are traversed in opposite senses. That antisymmetric combination is exactly what the ε\varepsilon-δ\delta identity turns into a component of the curl.

ProofAdaptedAdapted from Arfken, Weber & Harris (2013), §3.5, pp. 147–148 and pp. 150–151

Part 1. Fix k∈{1,2,3}k \in \{1,2,3\} and consider the two faces of QhQ_h normal to ek\mathbf{e}_k. On the far one the outward normal is +ek+\mathbf{e}_k and on the near one it is −ek-\mathbf{e}_k, so F⋅n^\mathbf{F}\cdot\hat{\mathbf{n}} is +Fk+F_k and −Fk-F_k respectively, and the two together contribute

∫Dh[Fk(y+hek)−Fk(y−hek)]dA(y),\int_{D_h}\Bigl[F_k(\mathbf{y} + h\mathbf{e}_k) - F_k(\mathbf{y} - h\mathbf{e}_k)\Bigr]dA(\mathbf{y}),

where y\mathbf{y} runs over the square DhD_h of side 2h2h through a\mathbf{a} normal to ek\mathbf{e}_k, of area 4h24h^2. Only the kkth component appears, because the other two components of F\mathbf{F} are tangent to these faces.

Apply the mean value theorem on [−h,h][-h,h] to t↦Fk(y+tek)t \mapsto F_k(\mathbf{y}+t\mathbf{e}_k), differentiable because F\mathbf{F} is C1C^1: the bracket equals 2h ∂kFk(ξ)2h\,\partial_kF_k(\boldsymbol{\xi}) for some point ξ\boldsymbol{\xi} on the segment joining the two face points, hence a point of QhQ_h. Write ∂kFk(ξ)=∂kFk(a)+ρk(y,h)\partial_kF_k(\boldsymbol{\xi}) = \partial_kF_k(\mathbf{a}) + \rho_k(\mathbf{y},h). The hypothesis that ∂kFk\partial_kF_k is continuous at a\mathbf{a} makes the error uniform: given ϵ>0\epsilon > 0 there is δ>0\delta > 0 with ∣∂kFk(x)−∂kFk(a)∣<ϵ|\partial_kF_k(\mathbf{x}) - \partial_kF_k(\mathbf{a})| < \epsilon whenever ∣x−a∣<δ|\mathbf{x}-\mathbf{a}| < \delta, and every point of QhQ_h satisfies this once h3<δh\sqrt{3} < \delta. So for such hh the pair of faces contributes

2h(4h2 ∂kFk(a)+∫Dhρk dA),∣∫Dhρk dA∣≤4h2ϵ.2h\Bigl(4h^2\,\partial_kF_k(\mathbf{a}) + \int_{D_h}\rho_k\, dA\Bigr), \qquad \Bigl|\int_{D_h}\rho_k\,dA\Bigr| \le 4h^2\epsilon .

Summing over k=1,2,3k = 1,2,3 and dividing by the volume 8h38h^3,

∣Φ(h)8h3−∂kFk(a)∣  ≤  3ϵfor all h<δ/3,\left|\frac{\Phi(h)}{8h^3} - \partial_kF_k(\mathbf{a})\right| \;\le\; 3\epsilon \qquad\text{for all } h < \delta/\sqrt{3},

and ∂kFk(a)=(∇⋅F)(a)\partial_kF_k(\mathbf{a}) = (\nabla\cdot\mathbf{F})(\mathbf{a}). As ϵ>0\epsilon > 0 was arbitrary, the limit is as claimed.

Part 2. Parametrise the four sides of ∂Sh\partial S_h in the anticlockwise sense determined by n^=u^×v^\hat{\mathbf{n}} = \hat{\mathbf{u}}\times\hat{\mathbf{v}} and write G(s,t)=F(a+su^+tv^)\mathbf{G}(s,t) = \mathbf{F}(\mathbf{a} + s\hat{\mathbf{u}} + t\hat{\mathbf{v}}). The tangent is +u^+\hat{\mathbf{u}} on the side t=−ht = -h, +v^+\hat{\mathbf{v}} on s=+hs = +h, −u^-\hat{\mathbf{u}} on t=+ht = +h and −v^-\hat{\mathbf{v}} on s=−hs = -h, so

C(h)=∫−hh[G(s,−h)−G(s,h)]⋅u^  ds  +  ∫−hh[G(h,t)−G(−h,t)]⋅v^  dt.C(h) = \int_{-h}^{h}\Bigl[\mathbf{G}(s,-h) - \mathbf{G}(s,h)\Bigr]\cdot\hat{\mathbf{u}}\; ds \;+\;\int_{-h}^{h}\Bigl[\mathbf{G}(h,t) - \mathbf{G}(-h,t)\Bigr]\cdot\hat{\mathbf{v}}\; dt .

Treat the two brackets by the mean value theorem exactly as in part 1, now along the directions v^\hat{\mathbf{v}} and u^\hat{\mathbf{u}}: the scalar functions t↦G(s,t)⋅u^t\mapsto \mathbf{G}(s,t)\cdot\hat{\mathbf{u}} and s↦G(s,t)⋅v^s \mapsto \mathbf{G}(s,t)\cdot\hat{\mathbf{v}} are differentiable with derivatives (v^⋅∇)(F⋅u^)(\hat{\mathbf{v}}\cdot\nabla)(\mathbf{F}\cdot\hat{\mathbf{u}}) and (u^⋅∇)(F⋅v^)(\hat{\mathbf{u}}\cdot\nabla)(\mathbf{F}\cdot\hat{\mathbf{v}}) evaluated along the segment, by the directional-derivative formula of Gradient and directional derivative. Hence the first bracket is −2h[(v^⋅∇)(F⋅u^)]-2h\bigl[(\hat{\mathbf{v}}\cdot\nabla)(\mathbf{F}\cdot\hat{\mathbf{u}})\bigr] and the second is +2h[(u^⋅∇)(F⋅v^)]+2h\bigl[(\hat{\mathbf{u}}\cdot\nabla)(\mathbf{F}\cdot\hat{\mathbf{v}})\bigr], each at some point of ShS_h. Continuity of the first partial derivatives of F\mathbf{F} at a\mathbf{a} again makes the replacement by their values at a\mathbf{a} uniform in the same sense, so dividing by the area 4h2=(2h)(2h)4h^2 = (2h)(2h) gives

lim⁡h→0+C(h)4h2=[(u^⋅∇)(F⋅v^)−(v^⋅∇)(F⋅u^)](a)=(u^iv^j−v^iu^j)∂iFj(a),\lim_{h\to 0^+}\frac{C(h)}{4h^2} = \bigl[(\hat{\mathbf{u}}\cdot\nabla)(\mathbf{F}\cdot\hat{\mathbf{v}}) - (\hat{\mathbf{v}}\cdot\nabla)(\mathbf{F}\cdot\hat{\mathbf{u}})\bigr](\mathbf{a}) = \bigl(\hat{u}_i\hat{v}_j - \hat{v}_i\hat{u}_j\bigr)\partial_iF_j(\mathbf{a}),

the constant vectors u^,v^\hat{\mathbf{u}}, \hat{\mathbf{v}} passing through the derivatives.

It remains to recognise this as (∇×F)⋅n^(\nabla\times\mathbf{F})\cdot\hat{\mathbf{n}}. Using (∇×F)k=εkij∂iFj(\nabla\times\mathbf{F})_k = \varepsilon_{kij}\partial_iF_j and n^k=εklmu^lv^m\hat{n}_k = \varepsilon_{klm}\hat{u}_l\hat{v}_m,

(∇×F)⋅n^=εkij εklm u^lv^m ∂iFj=(δilδjm−δimδjl) u^lv^m ∂iFj=(u^iv^j−v^iu^j)∂iFj,(\nabla\times\mathbf{F})\cdot\hat{\mathbf{n}} = \varepsilon_{kij}\,\varepsilon_{klm}\,\hat{u}_l\hat{v}_m\,\partial_iF_j = (\delta_{il}\delta_{jm} - \delta_{im}\delta_{jl})\,\hat{u}_l\hat{v}_m\,\partial_iF_j = \bigl(\hat{u}_i\hat{v}_j - \hat{v}_i\hat{u}_j\bigr)\partial_iF_j,

by the ε\varepsilon-δ\delta identity of Vector algebra and index notation after the cyclic shift εkij=εijk\varepsilon_{kij} = \varepsilon_{ijk}. The two expressions agree, which proves part 2. □\square

RemarkAdaptedAdapted from Riley, Hobson & Bence (2006), §10.7.3, Eq. (10.36), p. 353; Arfken, Weber & Harris (2013), §3.5, Eqs. (3.57) and (3.61), pp. 149 and 152

Where the hypotheses are used, and how to read the two operators.

  • Why C1C^1 and not just differentiability of the components. Mere existence of the partial derivatives defines ∇⋅F\nabla\cdot\mathbf{F} and ∇×F\nabla\times\mathbf{F} pointwise, but the two limits need the derivatives to be continuous at a\mathbf{a}: that is what allows the mean-value points ξ\boldsymbol{\xi}, which lie somewhere in the shrinking cube or square and are not under our control, to be replaced by a\mathbf{a} with a uniformly small error. Without continuity the mean values need not converge to the value at a\mathbf{a} and the flux-per-volume ratio can fail to have a limit, even though ∇⋅F(a)\nabla\cdot\mathbf{F}(\mathbf{a}) exists as a number.
  • Why cubes and squares, and what the limits really say. Nothing in the proof depends on the shapes beyond the fact that opposite faces or sides are parallel and a distance 2h2h apart. The statements are therefore the local, differential form of the two integral theorems: the global versions, over an arbitrary region and an arbitrary surface, are The divergence theorem and Stokes' theorem and Green's theorem in the plane, and part 2 is the reason the curl appears in Stokes' theorem contracted with the surface normal.
  • A rigid rotation has constant curl. Riley, Hobson and Bence (p. 353) take v=ω×r\mathbf{v} = \boldsymbol{\omega}\times\mathbf{r}, the velocity field of a body in rigid rotation at a constant rate ω\boldsymbol{\omega} about a fixed axis through the origin, and compute its curl: taking ω=ωe3\boldsymbol{\omega} = \omega\mathbf{e}_3 gives v=ω(−x2,x1,0)\mathbf{v} = \omega(-x_2, x_1, 0) and ∇×v=2ω e3=2ω\nabla\times\mathbf{v} = 2\omega\,\mathbf{e}_3 = 2\boldsymbol{\omega}. So the curl of a velocity field is twice the local angular velocity, not the angular velocity itself; the factor 22 is a standard trap, and the quantity ∇×v\nabla\times\mathbf{v} is called the vorticity. A paddle wheel held in the flow spins where the curl is non-zero and does not where it vanishes.
  • Irrotational and solenoidal are not opposites. They are independent conditions on different derivatives of F\mathbf{F}, and a field may satisfy both: in a charge-free region the electrostatic field has ∇⋅E=0\nabla\cdot\mathbf{E} = 0 and ∇×E=0\nabla\times\mathbf{E} = \mathbf{0}, which is what makes it the gradient of a harmonic potential (Harmonic functions).
  • Divergence-free does not mean "no flow". Arfken, Weber and Harris put it as: within a region where the divergence vanishes, the field lines entering a small volume must leave it, so they cannot begin or end there (p. 149). Field lines of a solenoidal field close up or run to the boundary.
NoteAdaptedAdapted from Hassani (2013), §21.2.2, p. 647; Arfken, Weber & Harris (2013), §3.6, Example 3.6.1, p. 154; Riley, Hobson & Bence (2006), §10.8.1, p. 355

The Laplacian of a radial function. If ϕ(x)=f(r)\phi(\mathbf{x}) = f(r) with r=∣x∣r = |\mathbf{x}| and ff twice differentiable, then for r>0r > 0

∇2ϕ=f′′(r)+m−1rf′(r)=1rm−1ddr(rm−1dfdr)in Rm,\nabla^2\phi = f''(r) + \frac{m-1}{r}f'(r) = \frac{1}{r^{m-1}}\frac{d}{dr}\Bigl(r^{m-1}\frac{df}{dr}\Bigr) \qquad\text{in } \mathbb{R}^m,

which for m=3m = 3 is f′′+2f′/rf'' + 2f'/r. Hassani derives this directly from ∂r/∂xi=xi/r\partial r/\partial x_i = x_i/r (p. 647), and Arfken, Weber and Harris get the m=3m=3 case by composing the radial gradient with the radial divergence (Example 3.6.1, p. 154). The extra first-order term is purely geometric: it is there because the direction r^\hat{\mathbf{r}} changes from point to point. Two consequences used constantly: ∇2rn=n(n+1)rn−2\nabla^2r^n = n(n+1)r^{n-2} in three dimensions, so rnr^n is harmonic exactly for n=0n = 0 and n=−1n = -1; and the Coulomb and Newton potentials 1/r1/r therefore satisfy Laplace's equation away from the origin, with everything concentrated at the origin, where none of these derivatives is defined (Fundamental solution of Laplace's equation).

ExampleOriginal

(a) The flux limit, computed exactly. Take F=(x3, y2, z2)\mathbf{F} = (x^3,\, y^2,\, z^2) and a=(1,1,1)\mathbf{a} = (1,1,1). From the definition,

∇⋅F=3x2+2y+2z,(∇⋅F)(a)=3+2+2=7.\nabla\cdot\mathbf{F} = 3x^2 + 2y + 2z, \qquad (\nabla\cdot\mathbf{F})(\mathbf{a}) = 3 + 2 + 2 = 7 .

Now compute the flux out of the cube QhQ_h of side 2h2h centred at a\mathbf{a} without taking any limit. On the two faces normal to e1\mathbf{e}_1 the integrand F1=x3F_1 = x^3 is constant, equal to (1±h)3(1\pm h)^3, and each face has area 4h24h^2, so that pair contributes

4h2[(1+h)3−(1−h)3]=4h2[2(3h+h3)]=24h3+8h5.4h^2\bigl[(1+h)^3 - (1-h)^3\bigr] = 4h^2\bigl[2(3h + h^3)\bigr] = 24h^3 + 8h^5 .

The faces normal to e2\mathbf{e}_2 contribute 4h2[(1+h)2−(1−h)2]=16h34h^2[(1+h)^2-(1-h)^2] = 16h^3, and those normal to e3\mathbf{e}_3 the same. Hence

Φ(h)=56h3+8h5,Φ(h)8h3=7+h2  →h→0+  7,\Phi(h) = 56h^3 + 8h^5, \qquad \frac{\Phi(h)}{8h^3} = 7 + h^2 \;\xrightarrow[h\to 0^+]{}\; 7 ,

matching (∇⋅F)(a)=7(\nabla\cdot\mathbf{F})(\mathbf{a}) = 7. The error h2h^2 comes from the third derivative of x3x^3, which survives the cancellation of even-order terms between opposite faces; for a field whose components are at most quadratic the ratio would be exactly the divergence for every hh.

(b) The circulation limit, computed exactly. Take G=(−y3, x3, 0)\mathbf{G} = (-y^3,\, x^3,\, 0), a=(1,0,0)\mathbf{a} = (1,0,0) and n^=e3\hat{\mathbf{n}} = \mathbf{e}_3, so u^=e1\hat{\mathbf{u}} = \mathbf{e}_1, v^=e2\hat{\mathbf{v}} = \mathbf{e}_2. Here

(∇×G)3=∂1G2−∂2G1=3x2+3y2,(∇×G)(a)⋅e3=3,(\nabla\times\mathbf{G})_3 = \partial_1G_2 - \partial_2G_1 = 3x^2 + 3y^2, \qquad (\nabla\times\mathbf{G})(\mathbf{a})\cdot\mathbf{e}_3 = 3 ,

and the other two components of ∇×G\nabla\times\mathbf{G} vanish identically. Round the square ∣x−1∣≤h|x-1|\le h, ∣y∣≤h|y| \le h anticlockwise, the two horizontal sides give ∫1−h1+h(−(−h)3) dx\int_{1-h}^{1+h}(-(-h)^3)\,dx and ∫1+h1−h(−h3) dx\int_{1+h}^{1-h}(-h^3)\,dx, that is 2h42h^4 each; the two vertical sides give (1+h)3⋅2h(1+h)^3\cdot 2h and (1−h)3⋅(−2h)(1-h)^3\cdot(-2h). Adding,

C(h)=4h4+2h[(1+h)3−(1−h)3]=4h4+2h(6h+2h3)=12h2+8h4,C(h) = 4h^4 + 2h\bigl[(1+h)^3 - (1-h)^3\bigr] = 4h^4 + 2h(6h + 2h^3) = 12h^2 + 8h^4,

so C(h)/(4h2)=3+2h2→3C(h)/(4h^2) = 3 + 2h^2 \to 3, again the predicted value. Note that G\mathbf{G} is not a rotation of any rigid body — its circulation density grows like 3(x2+y2)3(x^2+y^2) — yet the local statement needs no such structure.

(c) A Laplacian, and a harmonic function. For ϕ=x3−3xy2+z2\phi = x^3 - 3xy^2 + z^2,

∇2ϕ=6x−6x+2=2\nabla^2\phi = 6x - 6x + 2 = 2

everywhere, so ϕ\phi satisfies Poisson's equation ∇2ϕ=2\nabla^2\phi = 2 and the part x3−3xy2x^3-3xy^2 on its own is harmonic. For a radial check, put f(r)=1/rf(r) = 1/r in the note's formula with m=3m = 3: f′=−r−2f' = -r^{-2} and f′′=2r−3f'' = 2r^{-3}, so

∇21r=2r−3+2r(−r−2)=2r−3−2r−3=0(r>0),\nabla^2\frac1r = 2r^{-3} + \frac{2}{r}\bigl(-r^{-2}\bigr) = 2r^{-3} - 2r^{-3} = 0 \qquad (r > 0),

confirming by hand that the Coulomb potential is harmonic away from its singularity. Finally ∇×∇ϕ=0\nabla\times\nabla\phi = \mathbf{0} for this ϕ\phi, as the reader can check from ∇ϕ=(3x2−3y2, −6xy, 2z)\nabla\phi = (3x^2-3y^2,\, -6xy,\, 2z); that this is no accident is the content of Vector differential identities.

ApplicationAdaptedAdapted from Arfken, Weber & Harris (2013), §3.5, Eqs. (3.55)–(3.56), p. 148; Riley, Hobson & Bence (2006), §10.7.2, p. 352

Conservation laws are divergences. Let ρ(x,t)\rho(\mathbf{x},t) be the density of some conserved substance and v\mathbf{v} the velocity of the medium carrying it, so that ρv\rho\mathbf{v} is the flow rate per unit area. Part 1 of the statement identifies ∇⋅(ρv)\nabla\cdot(\rho\mathbf{v}) as how much leaves each unit of volume in each unit of time, so if the substance is neither created nor destroyed the amount inside a small volume can change only by flowing through its boundary, and

∂ρ∂t+∇⋅(ρv)=0\frac{\partial\rho}{\partial t} + \nabla\cdot(\rho\mathbf{v}) = 0

— the continuity equation, in the form Arfken, Weber and Harris derive from the flux of a volume element (Eq. (3.56), p. 148). The same equation with ρ\rho the electric charge density and J=ρv\mathbf{J} = \rho\mathbf{v} the current density is charge conservation; with a source term σ\sigma on the right it describes production or decay. For an incompressible fluid ρ\rho is constant and the equation collapses to ∇⋅v=0\nabla\cdot\mathbf{v} = 0: incompressibility is solenoidality, which is why the term appears in fluid mechanics at all. Combining the continuity equation with Fourier's law q=−k∇T\mathbf{q} = -k\nabla T of Gradient and directional derivative gives ρc ∂T/∂t=∇⋅(k∇T)=k∇2T\rho c\,\partial T/\partial t = \nabla\cdot(k\nabla T) = k\nabla^2T for constant kk, which is the heat equation of The heat kernel: this is where the Laplacian comes from physically, as the divergence of a gradient flux.

Curl measures what circulates. In fluid mechanics ζ=∇×v\boldsymbol{\zeta} = \nabla\times\mathbf{v} is the vorticity, and the remark's factor of 22 says that a fluid element in a region of uniform vorticity spins with angular velocity ζ/2\boldsymbol{\zeta}/2. In electromagnetism the two curl equations ∇×E=−∂B/∂t\nabla\times\mathbf{E} = -\partial\mathbf{B}/\partial t and ∇×B=μ0J+μ0ϵ0 ∂E/∂t\nabla\times\mathbf{B} = \mu_0\mathbf{J} + \mu_0\epsilon_0\,\partial\mathbf{E}/\partial t, together with ∇⋅B=0\nabla\cdot\mathbf{B} = 0 and ∇⋅E=ρ/ϵ0\nabla\cdot\mathbf{E} = \rho/\epsilon_0, are Maxwell's equations; read through this unit they say that the circulation of E\mathbf{E} round a small loop measures the rate of change of magnetic flux through it, that magnetic field lines have no sources, and that the flux of E\mathbf{E} out of a small volume measures the charge inside. Every one of the four is a statement about a flux density or a circulation density, which is why they take this form.

Method

Vector differential identities

Adapted

Collects the six product rules for ∇\nabla, ∇⋅\nabla\cdot and ∇×\nabla\times together with ∇×∇ϕ=0\nabla\times\nabla\phi = \mathbf{0}, ∇⋅(∇×a)=0\nabla\cdot(\nabla\times\mathbf{a}) = 0 and ∇×(∇×a)=∇(∇⋅a)−∇2a\nabla\times(\nabla\times\mathbf{a}) = \nabla(\nabla\cdot\mathbf{a}) - \nabla^2\mathbf{a}, and gives a mechanical index-notation recipe that proves each of them in a few lines.

Prerequisites: Partial derivatives and the chain rule, Vector algebra and index notation, Divergence, curl and the Laplacian

Method(Product rules)AdaptedAdapted from Riley, Hobson & Bence (2006), §10.8.1, Table 10.1, p. 354; Arfken, Weber & Harris (2013), §3.6, Eqs. (3.72)–(3.74), pp. 156–157

Let ϕ,ψ\phi, \psi be C1C^1 scalar fields and a,b\mathbf{a}, \mathbf{b} be C1C^1 vector fields on an open U⊆R3U \subseteq \mathbb{R}^3. (C1C^1 is more than the proof needs, and more than the source assumes: Riley, Hobson and Bence's Table 10.1, p. 354, asks only that the fields be differentiable. The first bullet of the remark below says what each identity actually uses and why we assume the stronger hypothesis here.) Then on UU:

∇(ϕψ)=ϕ ∇ψ+ψ ∇ϕ\nabla(\phi\psi) = \phi\,\nabla\psi + \psi\,\nabla\phi∇⋅(ϕa)=ϕ ∇⋅a+a⋅∇ϕ\nabla\cdot(\phi\mathbf{a}) = \phi\,\nabla\cdot\mathbf{a} + \mathbf{a}\cdot\nabla\phi∇×(ϕa)=∇ϕ×a+ϕ ∇×a\nabla\times(\phi\mathbf{a}) = \nabla\phi\times\mathbf{a} + \phi\,\nabla\times\mathbf{a}∇⋅(a×b)=b⋅(∇×a)−a⋅(∇×b)\nabla\cdot(\mathbf{a}\times\mathbf{b}) = \mathbf{b}\cdot(\nabla\times\mathbf{a}) - \mathbf{a}\cdot(\nabla\times\mathbf{b})∇×(a×b)=a(∇⋅b)−b(∇⋅a)+(b⋅∇)a−(a⋅∇)b\nabla\times(\mathbf{a}\times\mathbf{b}) = \mathbf{a}(\nabla\cdot\mathbf{b}) - \mathbf{b}(\nabla\cdot\mathbf{a}) + (\mathbf{b}\cdot\nabla)\mathbf{a} - (\mathbf{a}\cdot\nabla)\mathbf{b}∇(a⋅b)=a×(∇×b)+b×(∇×a)+(a⋅∇)b+(b⋅∇)a\nabla(\mathbf{a}\cdot\mathbf{b}) = \mathbf{a}\times(\nabla\times\mathbf{b}) + \mathbf{b}\times(\nabla\times\mathbf{a}) + (\mathbf{a}\cdot\nabla)\mathbf{b} + (\mathbf{b}\cdot\nabla)\mathbf{a}

Here (a⋅∇)(\mathbf{a}\cdot\nabla) is the scalar differential operator ai∂ia_i\partial_i of Gradient and directional derivative, applied to each Cartesian component of the field to its right: [(a⋅∇)b]i=aj ∂jbi\bigl[(\mathbf{a}\cdot\nabla)\mathbf{b}\bigr]_i = a_j\,\partial_jb_i. Each identity is linear in each field, and the three operators are linear, so ∇(ϕ+ψ)=∇ϕ+∇ψ\nabla(\phi+\psi) = \nabla\phi + \nabla\psi and likewise for ∇⋅\nabla\cdot and ∇×\nabla\times.

Method(Second-derivative identities)AdaptedAdapted from Arfken, Weber & Harris (2013), §3.6, pp. 154–155, including Eq. (3.70); Riley, Hobson & Bence (2006), §10.8.2, Eqs. (10.37)–(10.42), pp. 356–357

Let ϕ\phi be a C2C^2 scalar field and a\mathbf{a} a C2C^2 vector field on an open U⊆R3U \subseteq \mathbb{R}^3. Of the nine formal ways of applying two of ∇\nabla, ∇⋅\nabla\cdot, ∇×\nabla\times in succession, four are meaningless because the outer operator meets the wrong kind of field (∇(∇ϕ)\nabla(\nabla\phi), ∇⋅(∇⋅a)\nabla\cdot(\nabla\cdot\mathbf{a}), ∇×(∇⋅a)\nabla\times(\nabla\cdot\mathbf{a}), ∇(∇×a)\nabla(\nabla\times\mathbf{a})). Of the remaining five, two vanish identically:

∇×(∇ϕ)=0,∇⋅(∇×a)=0,\nabla\times(\nabla\phi) = \mathbf{0}, \qquad\qquad \nabla\cdot(\nabla\times\mathbf{a}) = 0 ,

one is the Laplacian, ∇⋅(∇ϕ)=∇2ϕ\nabla\cdot(\nabla\phi) = \nabla^2\phi, one is ∇(∇⋅a)\nabla(\nabla\cdot\mathbf{a}), and the last is given by the other two:

∇×(∇×a)=∇(∇⋅a)−∇2a,\nabla\times(\nabla\times\mathbf{a}) = \nabla(\nabla\cdot\mathbf{a}) - \nabla^2\mathbf{a},

where in Cartesian coordinates (∇2a)i=∇2ai(\nabla^2\mathbf{a})_i = \nabla^2a_i. Consequently every gradient field is irrotational and every curl field is solenoidal, and the converses hold locally on suitable domains (Helmholtz decomposition and potentials).

Idea of the proofOriginal

In index notation all three operators are one symbol, ∂i\partial_i, and all three products are built from δij\delta_{ij} and εijk\varepsilon_{ijk}. Once an identity is written out that way every object in sight is a number, so the only rules needed are the Leibniz rule for ∂i\partial_i of a product and the ε\varepsilon-δ\delta identity for a pair of ε\varepsilon's — and the terms may be rearranged freely, which is exactly what vector notation forbids. The product rules then come from Leibniz alone or from Leibniz plus one use of ε\varepsilon-δ\delta; the two vanishing identities come from contracting the antisymmetric εijk\varepsilon_{ijk} with the symmetric ∂j∂k\partial_j\partial_k; and ∇×(∇×a)\nabla\times(\nabla\times\mathbf{a}) comes from ε\varepsilon-δ\delta applied to two ε\varepsilon's with two derivatives between them.

Method(Proving a vector identity in index notation)AdaptedAdapted from Riley, Hobson & Bence (2006), §26.8, pp. 942–944
  1. Write the left-hand side in components, with one free index. Translate as you go: (a⋅b)→aibi(\mathbf{a}\cdot\mathbf{b}) \to a_ib_i, (a×b)i→εijkajbk(\mathbf{a}\times\mathbf{b})_i \to \varepsilon_{ijk}a_jb_k, (∇ϕ)i→∂iϕ(\nabla\phi)_i \to \partial_i\phi, ∇⋅a→∂iai\nabla\cdot\mathbf{a}\to\partial_ia_i, (∇×a)i→εijk∂jak(\nabla\times\mathbf{a})_i \to \varepsilon_{ijk}\partial_ja_k, [(a⋅∇)b]i→aj∂jbi\bigl[(\mathbf{a}\cdot\nabla)\mathbf{b}\bigr]_i \to a_j\partial_jb_i.
  2. Expand from the inside out, with fresh dummy indices every time. An index that is already free or already paired must not be reused; a third occurrence of any index means the expression is wrong, not merely ugly. Two ε\varepsilon's typically need four new indices, as in εijk∂j(εklm∂lam)\varepsilon_{ijk}\partial_j(\varepsilon_{klm}\partial_la_m).
  3. Differentiate the products by the Leibniz rule. Every factor is now a scalar function, so each derivative of a product splits into a sum of terms, and factors may be written in any order.
  4. Contract each pair of ε\varepsilon's with the ε\varepsilon-δ\delta identity of Vector algebra and index notation, arranging the shared index into the position εijkεklm\varepsilon_{ijk}\varepsilon_{klm} by cyclic shifts (which cost nothing) or transpositions (which cost a sign). Each resulting δ\delta renames one index and disappears.
  5. Translate back, reading the patterns of step 1 in reverse. The two patterns that most often cause trouble are ai ∂mbm=[a(∇⋅b)]ia_i\,\partial_mb_m = \bigl[\mathbf{a}(\nabla\cdot\mathbf{b})\bigr]_i and bm ∂mai=[(b⋅∇)a]ib_m\,\partial_ma_i = \bigl[(\mathbf{b}\cdot\nabla)\mathbf{a}\bigr]_i: they differ only in which index is summed, and confusing them swaps two terms of the answer.
  6. Check before trusting. The free indices on the two sides must be the same letters; every other index must appear exactly twice in each term; and the number of terms should match what step 4 produced (ε\varepsilon-δ\delta doubles the term count once per contraction).

When this is not enough. A term such as aj ∂ibja_j\,\partial_ib_j, which appears in the proof of the last product rule, is not a vector expression on its own: it is ∇\nabla acting on b\mathbf{b} only inside a⋅b\mathbf{a}\cdot\mathbf{b}, which vector notation cannot write. It must be paired with its partner bj∂iajb_j\partial_ia_j and the two combined into ∂i(ajbj)\partial_i(a_jb_j) before translating. Arfken, Weber and Harris handle the same obstacle in vector notation by introducing provisional symbols ∇A\nabla_{\mathbf{A}} and ∇B\nabla_{\mathbf{B}} for "∇\nabla acting on A\mathbf{A} only" and adding them (Example 3.6.4, p. 157); in index notation no such device is needed.

ProofAdaptedAdapted from Riley, Hobson & Bence (2006), §26.8, pp. 943–944; Arfken, Weber & Harris (2013), §3.6, pp. 154–157

Throughout, repeated indices are summed and all fields are evaluated at the same point. Each step names the rule it uses.

Product rules 1–3 (Leibniz only).

∂i(ϕψ)=ϕ ∂iψ+ψ ∂iϕ,\partial_i(\phi\psi) = \phi\,\partial_i\psi + \psi\,\partial_i\phi ,

which is the first identity. Next,

∇⋅(ϕa)=∂i(ϕai)=ϕ ∂iai+ai ∂iϕ=ϕ ∇⋅a+a⋅∇ϕ.\nabla\cdot(\phi\mathbf{a}) = \partial_i(\phi a_i) = \phi\,\partial_ia_i + a_i\,\partial_i\phi = \phi\,\nabla\cdot\mathbf{a} + \mathbf{a}\cdot\nabla\phi .

And

[∇×(ϕa)]i=εijk ∂j(ϕak)=εijk(∂jϕ)ak+ϕ εijk∂jak=(∇ϕ×a)i+ϕ (∇×a)i,\bigl[\nabla\times(\phi\mathbf{a})\bigr]_i = \varepsilon_{ijk}\,\partial_j(\phi a_k) = \varepsilon_{ijk}(\partial_j\phi)a_k + \phi\,\varepsilon_{ijk}\partial_ja_k = (\nabla\phi\times\mathbf{a})_i + \phi\,(\nabla\times\mathbf{a})_i ,

the first term being the cross product of the vector ∇ϕ\nabla\phi with a\mathbf{a} by step 1 of the method.

Product rule 4. Expand and apply Leibniz:

∇⋅(a×b)=∂i(εijkajbk)=εijk(∂iaj)bk+εijkaj(∂ibk).\nabla\cdot(\mathbf{a}\times\mathbf{b}) = \partial_i\bigl(\varepsilon_{ijk}a_jb_k\bigr) = \varepsilon_{ijk}(\partial_ia_j)b_k + \varepsilon_{ijk}a_j(\partial_ib_k).

In the first term shift the ε\varepsilon cyclically, εijk=εkij\varepsilon_{ijk} = \varepsilon_{kij}, to get bk εkij∂iaj=b⋅(∇×a)b_k\,\varepsilon_{kij}\partial_ia_j = \mathbf{b}\cdot(\nabla\times\mathbf{a}). In the second, one transposition gives εijk=−εjik\varepsilon_{ijk} = -\varepsilon_{jik}, so the term is −aj εjik∂ibk=− a⋅(∇×b)-a_j\,\varepsilon_{jik}\partial_ib_k = -\,\mathbf{a}\cdot(\nabla\times\mathbf{b}). Adding the two gives identity 4. (Equivalently: the left side is a scalar triple product of ∇\nabla, a\mathbf{a}, b\mathbf{b}, and the two terms are the two ways ∇\nabla can act.)

Product rule 5. Expand the inner cross product with fresh indices l,ml, m:

[∇×(a×b)]i=εijk ∂j(εklmalbm)=εijkεklm ∂j(albm).\bigl[\nabla\times(\mathbf{a}\times\mathbf{b})\bigr]_i = \varepsilon_{ijk}\,\partial_j\bigl(\varepsilon_{klm}a_lb_m\bigr) = \varepsilon_{ijk}\varepsilon_{klm}\,\partial_j(a_lb_m).

The two symbols already share kk in the right position, so the ε\varepsilon-δ\delta identity applies:

=(δilδjm−δimδjl) ∂j(albm)=∂m(aibm)−∂l(albi),= (\delta_{il}\delta_{jm} - \delta_{im}\delta_{jl})\,\partial_j(a_lb_m) = \partial_m(a_ib_m) - \partial_l(a_lb_i),

each δ\delta renaming one index. Leibniz on each term gives

=ai ∂mbm+bm ∂mai−bi ∂lal−al ∂lbi=[a(∇⋅b)+(b⋅∇)a−b(∇⋅a)−(a⋅∇)b]i,= a_i\,\partial_mb_m + b_m\,\partial_ma_i - b_i\,\partial_la_l - a_l\,\partial_lb_i = \bigl[\mathbf{a}(\nabla\cdot\mathbf{b}) + (\mathbf{b}\cdot\nabla)\mathbf{a} - \mathbf{b}(\nabla\cdot\mathbf{a}) - (\mathbf{a}\cdot\nabla)\mathbf{b}\bigr]_i ,

reading the four patterns of step 5 of the method. This is identity 5.

Product rule 6. Work backwards from the right-hand side, since that is where the two ε\varepsilon's are. By ε\varepsilon-δ\delta,

[a×(∇×b)]i=εijkaj εklm∂lbm=(δilδjm−δimδjl) aj ∂lbm=aj ∂ibj−aj ∂jbi,\bigl[\mathbf{a}\times(\nabla\times\mathbf{b})\bigr]_i = \varepsilon_{ijk}a_j\,\varepsilon_{klm}\partial_lb_m = (\delta_{il}\delta_{jm} - \delta_{im}\delta_{jl})\,a_j\,\partial_lb_m = a_j\,\partial_ib_j - a_j\,\partial_jb_i ,

that is aj∂ibj−[(a⋅∇)b]ia_j\partial_ib_j - \bigl[(\mathbf{a}\cdot\nabla)\mathbf{b}\bigr]_i. Exchanging the roles of a\mathbf{a} and b\mathbf{b},

[b×(∇×a)]i=bj ∂iaj−[(b⋅∇)a]i.\bigl[\mathbf{b}\times(\nabla\times\mathbf{a})\bigr]_i = b_j\,\partial_ia_j - \bigl[(\mathbf{b}\cdot\nabla)\mathbf{a}\bigr]_i .

Add the two. The leftover terms combine by Leibniz into aj∂ibj+bj∂iaj=∂i(ajbj)=[∇(a⋅b)]ia_j\partial_ib_j + b_j\partial_ia_j = \partial_i(a_jb_j) = \bigl[\nabla(\mathbf{a}\cdot\mathbf{b})\bigr]_i, which is precisely the combination step 6 of the method warns must be made before translating. Moving the two ( ⋅∇)(\,\cdot\nabla) terms to the other side gives identity 6.

The two vanishing identities. Both are the contraction of an antisymmetric symbol with a symmetric pair of derivatives.

[∇×(∇ϕ)]i=εijk ∂j∂kϕ.\bigl[\nabla\times(\nabla\phi)\bigr]_i = \varepsilon_{ijk}\,\partial_j\partial_k\phi .

Rename the dummy indices j↔kj \leftrightarrow k, which cannot change the value of a sum: the expression equals εikj∂k∂jϕ\varepsilon_{ikj}\partial_k\partial_j\phi. Now εikj=−εijk\varepsilon_{ikj} = -\varepsilon_{ijk} by antisymmetry, and ∂k∂jϕ=∂j∂kϕ\partial_k\partial_j\phi = \partial_j\partial_k\phi because ϕ\phi is C2C^2 (Partial derivatives and the chain rule) — this is the only place the hypothesis enters. So the expression equals minus itself and is therefore zero. The same two steps applied to

∇⋅(∇×a)=∂i(εijk∂jak)=εijk ∂i∂jak\nabla\cdot(\nabla\times\mathbf{a}) = \partial_i\bigl(\varepsilon_{ijk}\partial_ja_k\bigr) = \varepsilon_{ijk}\,\partial_i\partial_ja_k

give zero, now using the equality of mixed partials of each component aka_k.

Curl of a curl. Expand with fresh indices and contract:

[∇×(∇×a)]i=εijk ∂j(εklm∂lam)=(δilδjm−δimδjl) ∂j∂lam=∂j∂iaj−∂j∂jai.\bigl[\nabla\times(\nabla\times\mathbf{a})\bigr]_i = \varepsilon_{ijk}\,\partial_j\bigl(\varepsilon_{klm}\partial_la_m\bigr) = (\delta_{il}\delta_{jm} - \delta_{im}\delta_{jl})\,\partial_j\partial_la_m = \partial_j\partial_ia_j - \partial_j\partial_ja_i .

In the first term interchange the two derivatives, again by the C2C^2 hypothesis, to get ∂i(∂jaj)=[∇(∇⋅a)]i\partial_i(\partial_ja_j) = \bigl[\nabla(\nabla\cdot\mathbf{a})\bigr]_i; the second term is ∇2ai\nabla^2a_i. Hence ∇×(∇×a)=∇(∇⋅a)−∇2a\nabla\times(\nabla\times\mathbf{a}) = \nabla(\nabla\cdot\mathbf{a}) - \nabla^2\mathbf{a} with the Cartesian componentwise Laplacian. □\square

RemarkAdaptedAdapted from Arfken, Weber & Harris (2013), §3.6, pp. 154–155; Riley, Hobson & Bence (2006), §10.8, p. 354 and §10.8.2, pp. 356–357

Where the hypotheses are used, and what fails without them.

  • C1C^1 for the product rules, C2C^2 for the second-derivative identities. The product rules use only the Leibniz rule, so differentiability of each factor at the point suffices; C1C^1 is assumed above only so that the results are again continuous fields. The two vanishing identities and the curl-of-curl identity, by contrast, each rest on one interchange of mixed partial derivatives, and that is false in general without continuity of the second derivatives.
  • What goes wrong without C2C^2. Take f(x,y)=xy(x2−y2)/(x2+y2)f(x,y) = xy(x^2-y^2)/(x^2+y^2) off the origin, f(0,0)=0f(0,0) = 0, the function of the remark in Partial derivatives and the chain rule, for which ∂y∂xf(0,0)=−1\partial_y\partial_xf(0,0) = -1 and ∂x∂yf(0,0)=+1\partial_x\partial_yf(0,0) = +1. For the field ϕ(x,y,z)=f(x,y)\phi(x,y,z) = f(x,y), both mixed second derivatives exist at the origin, yet [∇×∇ϕ]3=∂x∂yϕ−∂y∂xϕ=2≠0\bigl[\nabla\times\nabla\phi\bigr]_3 = \partial_x\partial_y\phi - \partial_y\partial_x\phi = 2 \ne 0 there. So "∇×∇ϕ=0\nabla\times\nabla\phi = \mathbf{0}" is a theorem about C2C^2 fields, not a formal identity; Arfken, Weber and Harris flag the same point, writing that their determinant argument assumes the second derivatives may be taken in either order (p. 155) and holds when V\mathbf{V} has sufficient continuity.
  • The vector Laplacian is only componentwise in Cartesian coordinates. Riley, Hobson and Bence are explicit (pp. 356–357): ∇×(∇×a)=∇(∇⋅a)−∇2a\nabla\times(\nabla\times\mathbf{a}) = \nabla(\nabla\cdot\mathbf{a}) - \nabla^2\mathbf{a} holds in any coordinate system, but the further step (∇2a)i=∇2ai(\nabla^2\mathbf{a})_i = \nabla^2a_i requires the basis vectors to be constant, which fails for cylindrical and spherical polars, where the derivatives of the basis vectors contribute extra terms (Orthogonal curvilinear coordinates). Arfken, Weber and Harris take the opposite and cleaner route, defining ∇2V\nabla^2\mathbf{V} by that identity (Eq. (3.70), p. 155), so that the identity is true by construction and the componentwise formula is a Cartesian theorem. We follow Riley, Hobson and Bence in defining the Cartesian componentwise Laplacian first; the two conventions agree wherever both apply.
  • Why coordinate-free identities may be proved in Cartesian components. All of these were derived in one fixed orthonormal basis, yet they are asserted as identities between fields. The justification is that ∇ϕ\nabla\phi, ∇⋅a\nabla\cdot\mathbf{a} and ∇×a\nabla\times\mathbf{a} are themselves independent of which right-handed orthonormal basis is used — a scalar, a scalar and a vector respectively, as the flux and circulation characterisations of Divergence, curl and the Laplacian make clear without reference to coordinates. Riley, Hobson and Bence make the same remark (p. 354). The one place handedness matters is the sign of every curl, which flips in a left-handed basis along with the sign of ε123\varepsilon_{123}.
  • Two orderings that are genuinely different. ∇×(a×b)\nabla\times(\mathbf{a}\times\mathbf{b}) and (∇×a)×b(\nabla\times\mathbf{a})\times\mathbf{b} are unrelated expressions, so the brackets in identity 5 are load-bearing. The notation a⋅(∇b)\mathbf{a}\cdot(\nabla\mathbf{b}), by contrast, needs a convention rather than a warning. Fix the second-rank field ∇b\nabla\mathbf{b} by (∇b)ij=∂ibj(\nabla\mathbf{b})_{ij} = \partial_ib_j and let the dot contract its left (derivative) index; then ai ∂ibj=[(a⋅∇)b]ja_i\,\partial_ib_j = \bigl[(\mathbf{a}\cdot\nabla)\mathbf{b}\bigr]_j, so the two agree exactly, and the directional-derivative notation is just the contracted gradient. The expression that is not equal to either is the other contraction, aj ∂ibja_j\,\partial_ib_j: that is the term met in the proof of rule 6, and the closing paragraph of the method explains why vector notation cannot write it on its own.
ExampleOriginal

(a) Checking the curl-of-curl identity on a concrete field. Take

F=(x2y,  y2z,  xz2),P=(1,2,−1).\mathbf{F} = (x^2y,\; y^2z,\; xz^2), \qquad P = (1,2,-1).

Divergence and its gradient. ∇⋅F=2xy+2yz+2xz\nabla\cdot\mathbf{F} = 2xy + 2yz + 2xz, which at PP is 4−4−2=−24 - 4 - 2 = -2. Its gradient is ∇(∇⋅F)=(2y+2z,  2x+2z,  2y+2x)\nabla(\nabla\cdot\mathbf{F}) = (2y+2z,\; 2x+2z,\; 2y+2x), equal at PP to (2, 0, 6)(2,\,0,\,6).

Curl, then curl again. The components of the curl are ∂yF3−∂zF2=0−y2\partial_yF_3 - \partial_zF_2 = 0 - y^2, ∂zF1−∂xF3=0−z2\partial_zF_1 - \partial_xF_3 = 0 - z^2 and ∂xF2−∂yF1=0−x2\partial_xF_2 - \partial_yF_1 = 0 - x^2, so

∇×F=(−y2,  −z2,  −x2),(∇×F)(P)=(−4, −1, −1).\nabla\times\mathbf{F} = (-y^2,\; -z^2,\; -x^2), \qquad (\nabla\times\mathbf{F})(P) = (-4,\,-1,\,-1).

Taking the curl of that field by the same formula gives ∇×(∇×F)=(2z, 2x, 2y)\nabla\times(\nabla\times\mathbf{F}) = (2z,\, 2x,\, 2y), which at PP is (−2, 2, 4)(-2,\,2,\,4).

The Laplacian. Componentwise, ∇2(x2y)=2y\nabla^2(x^2y) = 2y, ∇2(y2z)=2z\nabla^2(y^2z) = 2z and ∇2(xz2)=2x\nabla^2(xz^2) = 2x, so ∇2F=(2y, 2z, 2x)\nabla^2\mathbf{F} = (2y,\,2z,\,2x), equal at PP to (4, −2, 2)(4,\,-2,\,2).

The identity. ∇(∇⋅F)−∇2F=(2y+2z−2y,  2x+2z−2z,  2y+2x−2x)=(2z, 2x, 2y)\nabla(\nabla\cdot\mathbf{F}) - \nabla^2\mathbf{F} = (2y+2z-2y,\; 2x+2z-2z,\; 2y+2x-2x) = (2z,\,2x,\,2y), which is exactly ∇×(∇×F)\nabla\times(\nabla\times\mathbf{F}) as fields, and at PP it is (2,0,6)−(4,−2,2)=(−2,2,4)(2,0,6) - (4,-2,2) = (-2,2,4), matching the direct computation.

(b) A product rule, checked. With ϕ=xy+z2\phi = xy + z^2 and a=(z, x, y)\mathbf{a} = (z,\,x,\,y): directly,

∇⋅(ϕa)=∂x[(xy+z2)z]+∂y[(xy+z2)x]+∂z[(xy+z2)y]=yz+x2+2yz=x2+3yz.\nabla\cdot(\phi\mathbf{a}) = \partial_x\bigl[(xy+z^2)z\bigr] + \partial_y\bigl[(xy+z^2)x\bigr] + \partial_z\bigl[(xy+z^2)y\bigr] = yz + x^2 + 2yz = x^2 + 3yz .

By the product rule, ∇⋅a=0\nabla\cdot\mathbf{a} = 0 and ∇ϕ=(y, x, 2z)\nabla\phi = (y,\,x,\,2z), so

ϕ ∇⋅a+a⋅∇ϕ=0+(zy+x⋅x+2yz)=x2+3yz,\phi\,\nabla\cdot\mathbf{a} + \mathbf{a}\cdot\nabla\phi = 0 + (zy + x\cdot x + 2yz) = x^2 + 3yz,

the same. Note how much less there is to differentiate on the second route.

(c) Deriving a new identity from rule 6. Put b=a\mathbf{b} = \mathbf{a} in the last product rule. The two cross-product terms become equal, and so do the two directional-derivative terms, so

∇(a⋅a)=2 a×(∇×a)+2 (a⋅∇)a,that is(a⋅∇)a=12∇∣a∣2−a×(∇×a).\nabla(\mathbf{a}\cdot\mathbf{a}) = 2\,\mathbf{a}\times(\nabla\times\mathbf{a}) + 2\,(\mathbf{a}\cdot\nabla)\mathbf{a}, \qquad\text{that is}\qquad (\mathbf{a}\cdot\nabla)\mathbf{a} = \tfrac12\nabla|\mathbf{a}|^2 - \mathbf{a}\times(\nabla\times\mathbf{a}).

Check it on a=(z,x,y)\mathbf{a} = (z,x,y). Here ∇×a=(1,1,1)\nabla\times\mathbf{a} = (1,1,1), so a×(∇×a)=(x−y,  y−z,  z−x)\mathbf{a}\times(\nabla\times\mathbf{a}) = (x-y,\; y-z,\; z-x); also 12∇(x2+y2+z2)=(x,y,z)\tfrac12\nabla(x^2+y^2+z^2) = (x,y,z); and (a⋅∇)a=(z∂x+x∂y+y∂z)(z,x,y)=(y, z, x)(\mathbf{a}\cdot\nabla)\mathbf{a} = (z\partial_x + x\partial_y + y\partial_z)(z,x,y) = (y,\,z,\,x). Then (x,y,z)−(x−y,y−z,z−x)=(y,z,x)(x,y,z) - (x-y,y-z,z-x) = (y,z,x), as the identity requires.

Example(A solenoidal field from two potentials)AdaptedAdapted from Riley, Hobson & Bence (2006), §10.8.2, Eq. (10.43), p. 357

This one is not ours: Riley, Hobson and Bence work it as the example closing their §10.8.2 (Eq. (10.43), p. 357), to show how two of the identities above combine, and we reproduce their route because it is the shortest there is.

Let ϕ\phi and ψ\psi be C2C^2 scalar fields. Product rule 4 holds for any pair of vector fields, so take the pair to be the two gradients, a=∇ϕ\mathbf{a} = \nabla\phi and b=∇ψ\mathbf{b} = \nabla\psi:

∇⋅(∇ϕ×∇ψ)=∇ψ⋅(∇×∇ϕ)−∇ϕ⋅(∇×∇ψ)=0,\nabla\cdot(\nabla\phi\times\nabla\psi) = \nabla\psi\cdot(\nabla\times\nabla\phi) - \nabla\phi\cdot(\nabla\times\nabla\psi) = 0 ,

since each bracket on the right is the curl of a gradient, which vanishes by the first of the two vanishing identities. (The C2C^2 hypothesis is needed exactly there, and nowhere else in the calculation.)

Two comments of our own. First, ∇ϕ×∇ψ\nabla\phi\times\nabla\psi is therefore solenoidal for every pair of potentials, which is a cheap way to manufacture divergence-free fields; the Clebsch representation v=∇ϕ×∇ψ\mathbf{v} = \nabla\phi\times\nabla\psi in fluid mechanics is this observation put to work. Second, the field is also tangent to both families of level surfaces {ϕ=const}\{\phi = \text{const}\} and {ψ=const}\{\psi = \text{const}\}, being orthogonal to both normals, so its field lines are the curves where those two families intersect.

ApplicationAdaptedAdapted from Arfken, Weber & Harris (2013), §3.6, Example 3.6.2 and Eqs. (3.70)–(3.71), pp. 155–156

The electromagnetic wave equation is one application of the curl-of-curl identity. In vacuum, with no charges or currents, Maxwell's equations read ∇⋅E=0\nabla\cdot\mathbf{E} = 0, ∇⋅B=0\nabla\cdot\mathbf{B} = 0, ∇×E=−∂B/∂t\nabla\times\mathbf{E} = -\partial\mathbf{B}/\partial t and ∇×B=ϵ0μ0 ∂E/∂t\nabla\times\mathbf{B} = \epsilon_0\mu_0\,\partial\mathbf{E}/\partial t. Take the curl of the third and substitute the fourth:

∇×(∇×E)=−∂∂t(∇×B)=−ϵ0μ0∂2E∂t2.\nabla\times(\nabla\times\mathbf{E}) = -\frac{\partial}{\partial t}\bigl(\nabla\times\mathbf{B}\bigr) = -\epsilon_0\mu_0\frac{\partial^2\mathbf{E}}{\partial t^2}.

Now the identity turns the left side into ∇(∇⋅E)−∇2E\nabla(\nabla\cdot\mathbf{E}) - \nabla^2\mathbf{E}, whose first term vanishes by the first equation. What is left is

∇2E=ϵ0μ0∂2E∂t2=1c2∂2E∂t2,\nabla^2\mathbf{E} = \epsilon_0\mu_0\frac{\partial^2\mathbf{E}}{\partial t^2} = \frac{1}{c^2}\frac{\partial^2\mathbf{E}}{\partial t^2},

three uncoupled scalar wave equations, one per Cartesian component, propagating at c=(ϵ0μ0)−1/2c = (\epsilon_0\mu_0)^{-1/2} (The wave equation in three dimensions); the same argument on B\mathbf{B} gives the same equation. This is Arfken, Weber and Harris's Example 3.6.2 (p. 156), and the step that does the work is the one that converts a curl of a curl into a Laplacian. The decoupling into components is Cartesian only, by the remark above.

The vanishing identities are why potentials exist. Because ∇⋅(∇×a)=0\nabla\cdot(\nabla\times\mathbf{a}) = 0, Maxwell's ∇⋅B=0\nabla\cdot\mathbf{B} = 0 is solved identically by B=∇×A\mathbf{B} = \nabla\times\mathbf{A} for a vector potential A\mathbf{A}; and because ∇×∇ϕ=0\nabla\times\nabla\phi = \mathbf{0}, that A\mathbf{A} is only determined up to A+∇χ\mathbf{A} + \nabla\chi — which is gauge freedom, read off from a vector identity. The same pair of identities gives the structure of Helmholtz decomposition and potentials and, in conservative mechanics, lets an irrotational force field be written as −∇V-\nabla V.

Fluid mechanics: the advective term is a gradient plus a vorticity term. The identity derived in part (c) of the example, applied to a velocity field v\mathbf{v} with vorticity ζ=∇×v\boldsymbol{\zeta} = \nabla\times\mathbf{v}, gives

(v⋅∇)v=∇(12∣v∣2)−v×ζ.(\mathbf{v}\cdot\nabla)\mathbf{v} = \nabla\Bigl(\tfrac12|\mathbf{v}|^2\Bigr) - \mathbf{v}\times\boldsymbol{\zeta}.

Substituting this into Euler's equation separates the nonlinear advective term into a part that is a pure gradient — which can be absorbed into the pressure head, giving Bernoulli's theorem along streamlines in steady irrotational flow — and a part that vanishes exactly when the flow is irrotational or the velocity is parallel to the vorticity. Taking the curl of the resulting equation and using ∇×∇(⋅)=0\nabla\times\nabla(\cdot) = \mathbf{0} eliminates both the pressure and the kinetic-energy head at a stroke and yields the vorticity equation, which is how the subject is usually set up.