Results 141 to 150 of about 24,807 (199)

Proper Efficiency in Nonconvex Multicriteria Programming

Mathematics of Operations Research, 1983
Proper efficient solutions of nonconvex vector maximum problems can be generated by solving a parametric family of ordinary nonlinear programs. This parametric scheme follows from the characterization of proper efficiency by an extended form of the generalized Tchebycheff norm.
Choo, E. U., Atkins, D. R.
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Semidefinite Programming Relaxation for Nonconvex Quadratic Programs

Journal of Global Optimization, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fujie, Tetsuya, Kojima, Masakazu
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Computing equilibria via nonconvex programming

Naval Research Logistics Quarterly, 1980
Abstract : The problem of determining a vector that places a system in a state of equilibrium is studied with the aid of mathematical programming. The approach derives from the logical equivalence between the general equilibrium problem and the complementarity problem.
Bard, Jonathan F., Falk, James E.
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Parallel computing in nonconvex programming

Annals of Operations Research, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pardalos, Panos M., Guisewite, G. M.
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Linearity Embedded in Nonconvex Programs

Journal of Global Optimization, 2005
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An Algorithm for Separable Nonconvex Programming Problems II: Nonconvex Constraints

Management Science, 1971
We extend a previous algorithm in order to solve mathematical programming problems of the form: Find x = (x1, …, xn) to minimize ∑φi0(xi) subject to x ∈ G, l ≦ x ≦ L and ∑φij(xi) ≦ 0, j = 1, …, m. Each φij is assumed to be lower semicontinuous, possibly nonconvex, and G is assumed to be closed.
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An Algorithm for Separable Nonconvex Programming Problems

Management Science, 1969
In this paper we present an algorithm for solving mathematical programming problems of the form: Find x = (x1,…, xn) to minimize ∑φi(xi) subject to x ∈ G and l ≤ x ≤ L. Each φi is assumed to be lower semicontinuous, possibly nonconvex, and G is assumed to be closed.
James E. Falk, Richard M. Soland
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Abstract nonsmooth nonconvex programming

1994
Various approximation results for compositions of locally Lipschitz functions are developed and used to extend known chain rules involving the Michel-Penot subdifferential. These results are combined with exact penalty function techniques to develop first order optimality conditions of the Karush-Kuhn-Tucker type for abstract cone-constrained ...
B. M. Glover, V. Jeyakumar
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Nonconvex Quadratic Programming via Generalized Polars

SIAM Journal on Applied Mathematics, 1975
A new approach is proposed to linearly constrained nonconvex quadratic programming. The approach is based on generalized polar sets, and is akin to the convex analysis approach to integer programming. We construct a generalized polar of the Kuhn–Tucker polyhedron associated with a quadratic program.
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