Explore: Conjugate Direction Methods

The biconjugate gradient method provides a generalization to non-symmetric matrices. Various nonlinear conjugate gradient methods seek minima of nonlinear

Powell's method, strictly Powell's conjugate direction method, is an algorithm proposed by Michael J. D. Powell for finding a local minimum of a function

explicitly. The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization

In numerical optimization, the nonlinear conjugate gradient method generalizes the conjugate gradient method to nonlinear optimization. For a quadratic

Methods based on Newton's method and inversion of the Hessian using conjugate gradient techniques can be better alternatives. Generally, such methods

Numerous methods exist to compute descent directions, all with differing merits, such as gradient descent or the conjugate gradient method. More generally

Finite Dimensional Case. Wiley. ISBN 9780471374718. ——— (1980). Conjugate Direction Methods in Optimization. Springer. ISBN 9783540904557. Landesman, Edward

NEWUOA LINCOA Nonlinear conjugate gradient method Levenberg–Marquardt algorithm Broyden–Fletcher–Goldfarb–Shanno or BFGS method Differential evolution

programming method (also called as Wilson–Han–Powell method), trust region algorithms (Powell's dog leg method), conjugate direction method (also called

the alternating-direction implicit (ADI) method is an iterative method used to solve Sylvester matrix equations. It is a popular method for solving the