- nonlinear programming algorithm
- алгоритм нелинейного программирования
Авиасловарь. М.А.Левин. 2004.
nonlinear programming algorithm
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Nonlinear programming — In mathematics, nonlinear programming (NLP) is the process of solving a system of equalities and inequalities, collectively termed constraints, over a set of unknown real variables, along with an objective function to be maximized or minimized,… … 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
Nonlinear dimensionality reduction — High dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lies on an embedded non linear manifold within… … 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
Levenberg–Marquardt algorithm — In mathematics and computing, the Levenberg–Marquardt algorithm (LMA)[1] provides a numerical solution to the problem of minimizing a function, generally nonlinear, over a space of parameters of the function. These minimization problems arise… … Wikipedia
Karmarkar's algorithm — is an algorithm introduced by Narendra Karmarkar in 1984 for solving linear programming problems. It was the first reasonably efficient algorithm that solves these problems in polynomial time. The ellipsoid method is also polynomial time but… … Wikipedia
Genetic algorithm — A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of… … Wikipedia
Frank–Wolfe algorithm — The Frank–Wolfe algorithm, also known as the convex combination algorithm , is a classic algorithm in operations research (OR). It was originally proposed by Marguerite Frank and Phil Wolfe in 1956 as a procedure for solving quadratic programming … Wikipedia
Sequential quadratic programming — (SQP) is one of the most popular and robust algorithms for nonlinear continuous optimization. The method is based on solving a series of subproblems designed to minimize a quadratic model of the objective subject to a linearization of the… … Wikipedia
BHHH algorithm — BHHH is an optimization algorithm in econometrics similar to Gauss–Newton algorithm. It is an acronym of the four originators: Berndt, B. Hall, R. Hall, and Jerry Hausman.UsageIf a nonlinear model is fitted to the data one often needs to estimate … 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
