Search results
Results from the WOW.Com Content Network
In mathematics, the method of descent is the term coined by the French mathematician Jacques Hadamard as a method for solving a partial differential equation in several real or complex variables, by regarding it as the specialisation of an equation in more variables, constant in the extra parameters.
The conjugate gradient method can be applied to an arbitrary n-by-m matrix by applying it to normal equations A T A and right-hand side vector A T b, since A T A is a symmetric positive-semidefinite matrix for any A. The result is conjugate gradient on the normal equations (CGN or CGNR). A T Ax = A T b
If the operator was self-adjoint, =, the direct state equation and the adjoint state equation would have the same left-hand side. In the goal of never inverting a matrix, which is a very slow process numerically, a LU decomposition can be used instead to solve the state equation, in O ( m 3 ) {\displaystyle O(m^{3})} operations for the ...
Whereas linear conjugate gradient seeks a solution to the linear equation =, the nonlinear conjugate gradient method is generally used to find the local minimum of a nonlinear function using its gradient alone. It works when the function is approximately quadratic near the minimum, which is the case when the function is twice differentiable at ...
From the equation for H, one sees that 1 + x y ′ > 0. Since x > 0, it follows that y ′ ≥ 0. Hence the point (x, y ′) is in the first quadrant. By reflection, the point (y ′, x) is also a point in the first quadrant on H. Moreover from Vieta's formulas, yy ′ = x 2 - q, and y ′ = x 2 - q / y . Combining this equation with x ...
Gradient descent can also be used to solve a system of nonlinear equations. Below is an example that shows how to use the gradient descent to solve for three unknown variables, x 1, x 2, and x 3. This example shows one iteration of the gradient descent. Consider the nonlinear system of equations
is calculated from (,,) by considering a variable weight and applying gradient descent to the function ((,),) to find a local minimum, starting at =. This makes w 1 {\displaystyle w_{1}} the minimizing weight found by gradient descent.
In mathematics, the method of steepest descent or saddle-point method is an extension of Laplace's method for approximating an integral, where one deforms a contour integral in the complex plane to pass near a stationary point (saddle point), in roughly the direction of steepest descent or stationary phase. The saddle-point approximation is ...