maths.free › Multivariable Calculus › Optimisation in several variables

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.

වැඩ කළ උදාහරණය · 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

පියවරෙන් පියවර

  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.

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

දැන් ඔයා ප් රශ්නයක් තෝරන්න, හෝ වර්ගය හෝ ඔබේම අඳින්න. සෑම පියවරක්, පින්තූරයක්, ඔබ ඉල්ලා තෙක් පිළිතුර සැඟවුණු.

ඔයාගෙ වැඩේ කරගෙන යන්න

නිදහස් ගිණුමක් සෑම පාඩමක් මත සටහන් එකතු, ඔබ අවසන් කර ඇති දේ වාර්තාවක්, එක් ස්ථානයක ඔබේ විසඳා ගැටළු, හා ඔබ මෙම පිටුව ගැන විමසීමට හැකි ගුරුවරයෙකු. ගණිතය ම සියලු දෙනාට විවෘත වේ, ඇතුලත් හෝ නැත.

ලියාපදිංචි වන්න පිවිසුම්

මෙහිදී භාවිත කරන සංකේත

සම්පූර්ණ අර්ථ දැක්වීම සඳහා ඕනෑම සංකේතයක් ටැප්, පින්තූරයක්, සහ එය සෑම අකුරු අදහස් කරන්නේ කුමක්ද.

කොහොමද: 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.

ජනතාව අහනවා ප්රශ්න

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.

තවත් Multivariable Calculus

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