Gradient
Builds on Differentiable function of several variablesPartial derivativeEuclidean space
Definition
Let be open, let , and let be a point at which is differentiable. The gradient of at is the unique vector such that where is the dot product of Euclidean space. In coordinates, it is the vector of partial derivatives Other notations are and , where is the del operator.
Remarks
The vector exists and is unique because each linear map is for exactly one . For , the gradient is . A gradient is a column vector, and the derivative is the row vector of the same entries. Some books do not make this distinction. The map is a vector field on , the gradient field of .
Reading path
To understand this page from the ground up, read these 7 pages in order. The foundations come first.
Show the path as a graph
- no evidence level
- Euclidean space object
- Function concept
- Function of several variables concept
- Limit of a function of several variables concept
- Partial derivative concept
- Differentiable function of several variables definition
- Gradient definition
Where this leads
71 pages build on this one.
- Exterior derivative
- Saddle point
- Mean value theorem in several variables
- Lagrange multiplier with one constraint
- Poincaré lemma for one-forms on convex sets
- Conservative vector field
- Laplacian as a sum of second partial derivatives
- Stokes' theorem
- Curl-free vector field on a convex set is conservative
- Path independence of line integrals
- Tangent plane and normal vector of a surface
- Surface integral
- Orientable surface
- Closed and exact forms
- Derivative vanishes at a local extremum
- Scalar potential
- Curl-free field that is not conservative
- Second derivative test in several variables
- Generalised Stokes theorem
- Vector calculus identities
- Divergence theorem
- Curl of a gradient is zero
- One-form
- Second-order Taylor expansion
- Gradient theorem
- Lagrange multipliers
- Gradient is orthogonal to level sets
- Harmonic function
- Surface area
- The exterior derivative squares to zero
- Laplacian
- Flux through a surface
- Homotopy invariance of line integrals of closed forms
- Gradient points in the direction of steepest ascent
- Geodesic curvature
- Shell theorem
- Cheng–Yau gradient estimate
- Removable singularities for harmonic functions
- Jensen's formula
- Harmonic majorant
- Shape operator
- Score
- Brelot space
- Poisson–Jensen formula
- Second fundamental form
- Smoothness and analyticity of harmonic functions
- Symplectic manifold
- First fundamental form
- Sub-mean value property
- Harmonic measure
- Hermitian manifold
- Double layer potential
- Koebe's theorem on the mean value property
- Area-minimizing current
- Compactness of families of harmonic functions
- Gradient estimates for harmonic functions
- Harnack's principle
- Poisson integral formula
- Green's identities
- Least harmonic majorant
- First main theorem of Nevanlinna theory
- Liouville's theorem for harmonic functions
- Liouville's theorem for planar harmonic functions
- Gauss's law for potentials
- Tautological one-form
- Graph embedding
- Parabolic and hyperbolic Riemann surfaces
- Poisson integral for the half-space
- Harmonic space
- Bôcher's theorem
- Hartogs lemma for subharmonic functions
Further reading
- Web https://en.wikipedia.org/wiki/Gradient, Definition
- Web https://en.wikipedia.org/wiki/Gradient, Cartesian coordinates
About this page
| Tag | 0NS2QYVC4Y |
|---|---|
| URI | m2://0NS2QYVC4Y |
| Kind | definition |
| Writing | W1 stated |
| MSC | 26B12 |
| Defines | Gradient |
| Uses | Differentiable function of several variables, Partial derivative, Euclidean space |
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What links here
Used by
- Exterior derivative
- Saddle point
- Mean value theorem in several variables
- Lagrange multiplier with one constraint
- Conservative vector field
- Laplacian as a sum of second partial derivatives
- Tangent plane and normal vector of a surface
- Derivative vanishes at a local extremum
- Scalar potential
- Vector calculus identities
- Curl of a gradient is zero
- Second-order Taylor expansion
- Gradient theorem
- Lagrange multipliers
- Gradient is orthogonal to level sets
- Laplacian
- Gradient points in the direction of steepest ascent
- Score