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augmented Lagrangian method

См. также в других словарях:

  • Cutting-plane method — In mathematical optimization, the cutting plane method is an umbrella term for optimization methods which iteratively refine a feasible set or objective function by means of linear inequalities, termed cuts. Such procedures are popularly used to… …   Wikipedia

  • Newton's method — In numerical analysis, Newton s method (also known as the Newton–Raphson method), named after Isaac Newton and Joseph Raphson, is a method for finding successively better approximations to the roots (or zeroes) of a real valued function. The… …   Wikipedia

  • Iterative method — In computational mathematics, an iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems. A specific implementation of an iterative method, including the termination… …   Wikipedia

  • Newton's method in optimization — A comparison of gradient descent (green) and Newton s method (red) for minimizing a function (with small step sizes). Newton s method uses curvature information to take a more direct route. In mathematics, Newton s method is an iterative method… …   Wikipedia

  • Nelder–Mead method — Nelder–Mead simplex search over the Rosenbrock banana function (above) and Himmelblau s function (below) See simplex algorithm for Dantzig s algorithm for the problem of linear opti …   Wikipedia

  • Nonlinear conjugate gradient method — In numerical optimization, the nonlinear conjugate gradient method generalizes the conjugate gradient method to nonlinear optimization. For a quadratic function : The minimum of f is obtained when the gradient is 0: . Whereas linear conjugate… …   Wikipedia

  • Category:Optimization algorithms — An optimization algorithm is an algorithm for finding a value x such that f(x) is as small (or as large) as possible, for a given function f, possibly with some constraints on x. Here, x can be a scalar or vector of continuous or discrete values …   Wikipedia

  • Semidefinite programming — (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine space.Semidefinite programming is a relatively new field… …   Wikipedia

  • PENOPT — is an optimization software package. Its goal is to develop a unified approach to problems of nonlinear programming and (linear and nonlinear) semidefinite programming. The solvers are based on a generalized augmented Lagrangian method combined… …   Wikipedia

  • Linear programming — (LP, or linear optimization) is a mathematical method for determining a way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model for some list of requirements represented as linear relationships.… …   Wikipedia

  • Convex optimization — Convex minimization, a subfield of optimization, studies the problem of minimizing convex functions over convex sets. Given a real vector space X together with a convex, real valued function defined on a convex subset of X, the problem is to find …   Wikipedia

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