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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.

ຕົວຢ່າງ​ທີ່​ໄດ້​ເຮັດ · 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

ຂັ້ນ​ຕອນ​ຕໍ່​ຂັ້ນ​ຕອນ

  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.

ເປີດ​ເຜີຍ​ຄຳ​ຕອບ
\nabla f = \left[\begin{matrix}2 x - 2\\2 y - 4\end{matrix}\right]

ປັດຈຸບັນ​ທ່ານ ເລືອກ​ບັນຫາ ຫຼື ພິມ ຫຼື ແຕ້ມ​ເອງ. ທຸກ​ບາດກ້າວ, ຮູບ, ຄໍາຕອບ​ຖືກ​ລົບ​ຈົນ​ກວ່າ​ເຈົ້າ​ຈະ​ຖາມ​ມັນ.

ຮັກສາ​ການ​ເຮັດວຽກ​ຂອງ​ທ່ານ​ໄວ້

ບັນຊີຟຣີ ເພີ່ມ​ຂໍ້​ມູນ​ໃສ່​ບົດຮຽນ​ທຸກ​ບົດ, ບັນທຶກ​ຂອງ​ສິ່ງທີ່​ທ່ານ​ໄດ້​ເຮັດ​ແລ້ວ, ບັນຫາ​ທີ່​ທ່ານ​ໄດ້​ແກ້​ໄຂ​ໄວ້​ໃນ​ບ່ອນ​ດຽວ ແລະ ຄູ​ສອນ​ທີ່​ທ່ານ​ສາມາດ​ຖາມ​ກ່ຽວກັບ​ໜ້າ​ນີ້. ຄະນິດສາດ​ເອງ​ແມ່ນ​ເປີດ​ໃຫ້​ທຸກ​ຄົນ, ບໍ່​ວ່າ​ຈະ​ໄດ້​ເຂົ້າ​ສູ່​ລະບົບ ຫຼື ບໍ່.

ລົງທະບຽນ ចូល

ຕົວ​ສະ​ແດງ​ທີ່​ໃຊ້​ຢູ່​ທີ່​ນີ້

ກົດ​ຕົວ​ອັກສອນ​ໃດ​ໜຶ່ງ​ເພື່ອ​ເບິ່ງ​ຄວາມ​ໝາຍ​ເຕັມ, ຮູບ ແລະ ຕົວອັກສອນ​ແຕ່ລະ​ຕົວ​ໃນ​ມັນ​ໝາຍ​ຄວາມ​ວ່າ​ແນວໃດ.

ວິທີການ: 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.

ຄໍາຖາມທີ່ຄົນຖາມ

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.

ເພີ່ມເຕີມໃນ Multivariable Calculus

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