Difference between revisions of "Gradient"
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| − | In [[mathematics]], the gradient of a | + | In [[mathematics]], the '''gradient''' is a [[vector]] associated to a point <math>p</math> of a [[differentiable]] [[function]] <math>f(x_1,...,x_n)</math> which takes [[real]] values. Specifically, the gradient at <math>p</math> is a vector in <math>R^n</math> which points in the direction in which <math>f</math> increases most rapidly at <math>p</math>. The magnitude of the gradient at <math>p</math> is equal to the maximum [[directional derivative]] of <math>f</math> at <math>p</math>. The gradient is an extension of the idea of [[derivative]] to functions with more than one [[variable]]. |
| − | More precisely, we define the gradient, <math>\nabla f</math> of <math>f</math> to be the vector | + | More precisely, we define the gradient, <math>\nabla f</math> of <math>f</math> to be the [[vector field]]: |
<math> | <math> | ||
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</math> | </math> | ||
| − | If <math>u</math> is a unit vector in <math>R^n</math>, then, by the chain rule, the directional derivative of <math>f</math> in the direction of <math>u</math> is simply the dot product: | + | consisting of the various [[partial derivative]]s of <math>f</math>. If <math>u</math> is a [[unit vector]] in <math>R^n</math>, then, by the [[chain rule]], the directional derivative of <math>f</math> in the direction of <math>u</math> is simply the [[dot product]]: |
<math> | <math> | ||
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</math> | </math> | ||
| − | Evidently by the Cauchy-Schwartz inequality, the directional derivative in the direction <math>u</math> is maximal in the direction of the gradient, and equal to <math>||\nabla f||</math> for <math>u</math> a unit vector in the direction of the gradient. | + | Evidently by the [[Cauchy-Schwartz inequality]], the directional derivative in the direction <math>u</math> is maximal in the direction of the gradient, and equal to <math>||\nabla f||</math> for <math>u</math> a unit vector in the direction of the gradient. |
==Properties of the Gradient== | ==Properties of the Gradient== | ||
| − | If <math>f</math> is a differentiable function with smooth level | + | If <math>f</math> is a differentiable function with smooth [[level set]]s <math>f^{-1}(c)</math>, then the gradient vector field <math>\nabla f</math> is [[perpendicular]] to the level sets of <math>f</math>. For fix a level set <math>S = f^{-1}(c)</math>, and let <math>v</math> be a vector [[tangent]] to <math>S</math> at <math>p</math>. Then we can find a [[curve]] <math>\gamma(t)</math> on <math>S</math> with <math>\gamma'(0) = v</math>. Now |
<math> | <math> | ||
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Thus, <math>\nabla f</math> is perpendicular to <math>v</math> at <math>p</math>, i.e., the gradient of <math>f</math> is perpendicular to the level sets of <math>f</math>. | Thus, <math>\nabla f</math> is perpendicular to <math>v</math> at <math>p</math>, i.e., the gradient of <math>f</math> is perpendicular to the level sets of <math>f</math>. | ||
| − | [[Category: | + | |
| + | [[Category:calculus]] | ||
Revision as of 16:54, July 14, 2008
| <math>\pi_1(S^1)=?\,</math> | This article/section deals with mathematical concepts appropriate for a student in late university or graduate level. |
In mathematics, the gradient is a vector associated to a point <math>p</math> of a differentiable function <math>f(x_1,...,x_n)</math> which takes real values. Specifically, the gradient at <math>p</math> is a vector in <math>R^n</math> which points in the direction in which <math>f</math> increases most rapidly at <math>p</math>. The magnitude of the gradient at <math>p</math> is equal to the maximum directional derivative of <math>f</math> at <math>p</math>. The gradient is an extension of the idea of derivative to functions with more than one variable.
More precisely, we define the gradient, <math>\nabla f</math> of <math>f</math> to be the vector field:
<math> \nabla f = (\frac{\partial f}{\partial x_1},...,\frac{\partial f}{\partial x_n}) </math>
consisting of the various partial derivatives of <math>f</math>. If <math>u</math> is a unit vector in <math>R^n</math>, then, by the chain rule, the directional derivative of <math>f</math> in the direction of <math>u</math> is simply the dot product:
<math> \nabla f \cdot u </math>
Evidently by the Cauchy-Schwartz inequality, the directional derivative in the direction <math>u</math> is maximal in the direction of the gradient, and equal to <math>||\nabla f||</math> for <math>u</math> a unit vector in the direction of the gradient.
Properties of the Gradient
If <math>f</math> is a differentiable function with smooth level sets <math>f^{-1}(c)</math>, then the gradient vector field <math>\nabla f</math> is perpendicular to the level sets of <math>f</math>. For fix a level set <math>S = f^{-1}(c)</math>, and let <math>v</math> be a vector tangent to <math>S</math> at <math>p</math>. Then we can find a curve <math>\gamma(t)</math> on <math>S</math> with <math>\gamma'(0) = v</math>. Now
<math> f\circ\gamma(t) = c </math>
since <math>S</math> is a level set. Taking derivatives of both sides and applying the chain rule, we get that
<math> \nabla f\cdot \gamma'(0) = \nabla f\cdot v = 0 </math>
Thus, <math>\nabla f</math> is perpendicular to <math>v</math> at <math>p</math>, i.e., the gradient of <math>f</math> is perpendicular to the level sets of <math>f</math>.