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Optimisation in several variables

Critical points, the second-derivative test, and Lagrange multipliers.

Critical points are where the gradient vanishes; the Hessian's determinant sorts them into minima, maxima and saddles. With a constraint, Lagrange multipliers say the gradients of f and of the constraint are parallel at the optimum. Picture it: the surface's bowl bottom, hilltop or saddle. The 3D view shows which. Think it: constrained optimisation is the geometry of tangency between level sets.

Çözümlü örnek · gradient of x^2 + y^2 - 2x - 4y

Multivariable-Calculus · Partial-Derivatives

Gradient of x^2 - 2x + y^2 - 4y

x^{2} - 2 x + y^{2} - 4 y

Adım adım

  1. f(x, y) = x^{2} - 2 x + y^{2} - 4 y

    The gradient is the vector of partial derivatives. Differentiate with respect to each variable, holding the others constant.

  2. \frac{\partial f}{\partial x} = 2 x - 2

    Treat every variable except x as a constant.

  3. \frac{\partial f}{\partial y} = 2 y - 4

    Treat every variable except y as a constant.

  4. \nabla f = \left[\begin{matrix}2 x - 2\\2 y - 4\end{matrix}\right]

    Assemble the gradient vector. It points in the direction of steepest ascent.

  5. (1, 2)

    Critical points: where every partial derivative is zero.

Cevabı göster
\nabla f = \left[\begin{matrix}2 x - 2\\2 y - 4\end{matrix}\right]

Şimdi sen Bir problem seçin, ya da kendi probleminizi yazın ya da çizin. Her adım, bir resim, cevap sorulana kadar gizli.

Kendi çalışmalarınızı saklayın

Ücretsiz bir hesap her ders için notlar, bitirdiğiniz şeylerin kaydı, çözüldüğünüz problemlerin bir yerdeki kaydı ve bu sayfa hakkında soru sormak için bir öğretmen ekler. Matematik kendisi, giriş yapmış veya yapmamış herkese açıktır.

Kayıt Ol Giriş

Burada kullanılan sembolleri

Tüm tanım, resim ve her harfin anlamı için herhangi bir sembolü tıklayın.

Nasıl: Optimisation in several variables

  1. The gradient is the vector of partial derivatives. Differentiate with respect to each variable, holding the others constant.
  2. Treat every variable except x as a constant.
  3. Treat every variable except y as a constant.
  4. Assemble the gradient vector. It points in the direction of steepest ascent.
  5. Critical points: where every partial derivative is zero.

Sık sorulan sorular

What is a partial derivative?

The ordinary derivative with respect to one variable while every other variable is frozen: the slope of the surface in one coordinate direction.

What does the gradient point at?

Uphill: the direction of steepest increase, with length equal to that steepest slope. It is perpendicular to the level curves.

More in Multivariable Calculus

Functions of several variablesPartial derivatives and the gradientDouble and triple integralsVector fields, line integrals and the big theorems