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A New Global Optimization Scheme for Quadratic Programs with Low-Rank Nonconvexity

INFORMS Journal on Computing, 2021
We consider the classical convex constrained nonconvex quadratic programming problem where the Hessian matrix of the objective to be minimized has r negative eigenvalues, denoted by (QPr). Based on a biconvex programming reformulation in a slightly higher dimension, we propose a novel branch-and-bound algorithm to solve (QP1) and show that it returns ...
Xiaoli Cen, Yong Xia 0002
openaire   +3 more sources

Global optimization of nonconvex factorable programming problems

Mathematical Programming, 2001
In this paper is presented a global optimization approach for solving a class of nonconvex factorable programming problems, that arise in a variety of engineering process control and design problems. McCormick introduced the nonconvex factorable programming problem in 1976 in a different, but equivalent, form.
Hanif D. Sherali, Hongjie Wang
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Global optimization of a rank-two nonconvex program

Mathematical Methods of Operations Research, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
CAMBINI, RICCARDO, SODINI, CLAUDIO
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A global optimization method for nonconvex separable programming problems

European Journal of Operational Research, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Han-Lin Li 0003, Chian-Son Yu
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On Global Linear Convergence in Stochastic Nonconvex Optimization for Semidefinite Programming

IEEE Transactions on Signal Processing, 2019
Nonconvex reformulations via low-rank factorization for stochastic convex semidefinite optimization problem have attracted arising attention due to their empirical efficiency and scalability. Compared with the original convex formulations, the nonconvex ones typically involve much fewer variables, allowing them to scale to scenarios with millions of ...
Jinshan Zeng, Ke Ma 0001, Yuan Yao 0011
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Global optimality conditions for mixed nonconvex quadratic programs†

Optimization, 2009
In this article, we present some global optimality conditions for mixed quadratic programming problems. Our approach is based on a L-subdifferential and an associated L-normal cone. Unlike most subdifferentials, the L-subdifferential is formed by functions that are not necessarily linear functions.
Z.Y. Wu, F.S. Bai
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Global Optimization Versus Integer Programming in Portfolio Optimization under Nonconvex Transaction Costs

Journal of Global Optimization, 2005
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Hiroshi Konno, Rei Yamamoto
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Global optimization of nonconvex nonlinear programs via interval analysis

Computers & Chemical Engineering, 1994
Abstract A new global-optimization procedure is devised to tackle nonconvex nonlinear programming problems. The proposed algorithm is based on interval analysis and is guaranteed to yield the global solution. Several new accelerating tools are introduced to significantly reduce the computational intensity associated with classical interval-based ...
R. Vaidyanathan, M. El-Halwagi
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Global Optimization of Nonconvex Generalized Disjunctive Programs

2009
Abstract This paper is concerned with the global optimization of Bilinear and Concave Generalized Disjunctive Programs. The efficiency of methods to solve these problems relies heavily on their capability for predicting strong lower bounds that in turn depend on the strength of their relaxations.
Juan P. Ruiz, Ignacio E. Grossmann
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Sufficient Conditions for Global Optimality of Bivalent Nonconvex Quadratic Programs with Inequality Constraints

Journal of Optimization Theory and Applications, 2007
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Wu, Z. Y., Jeyakumar, V., Rubinov, A. M.
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