Results 181 to 190 of about 1,124 (204)
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IEEE Transactions on Cybernetics
A swarm-exploring neurodynamic network (SENN) based on a two-timescale model is proposed in this study for solving nonconvex nonlinear programming problems. First, by using a convergent-differential neural network (CDNN) as a local quadratic programming (QP) solver and combining it with a two-timescale model design method, a two-timescale convergent ...
Yamei Luo +5 more
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A swarm-exploring neurodynamic network (SENN) based on a two-timescale model is proposed in this study for solving nonconvex nonlinear programming problems. First, by using a convergent-differential neural network (CDNN) as a local quadratic programming (QP) solver and combining it with a two-timescale model design method, a two-timescale convergent ...
Yamei Luo +5 more
openaire +2 more sources
INFORMS Journal on Computing
We learn optimal instance-specific heuristics for the global minimization of nonconvex quadratically constrained quadratic programs (QCQPs). Specifically, we consider partitioning-based convex mixed-integer programming relaxations for nonconvex QCQPs and propose the novel problem of strong partitioning to optimally partition variable domains without ...
Rohit Kannan +2 more
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We learn optimal instance-specific heuristics for the global minimization of nonconvex quadratically constrained quadratic programs (QCQPs). Specifically, we consider partitioning-based convex mixed-integer programming relaxations for nonconvex QCQPs and propose the novel problem of strong partitioning to optimally partition variable domains without ...
Rohit Kannan +2 more
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Distributed Global Optimization for a Class of Nonconvex Optimization With Coupled Constraints
IEEE Transactions on Automatic Control, 2022Xiaoxing Ren, Yugeng Xi, Dewei Li
exaly
2014
The primary objective of this thesis is to develop and implement a global optimization algorithm to solve a class of nonconvex programming problems, and to test it using a collection of engineering design problem applications.The class of problems we consider involves the optimization of a general nonconvex factorable objective function over a feasible
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The primary objective of this thesis is to develop and implement a global optimization algorithm to solve a class of nonconvex programming problems, and to test it using a collection of engineering design problem applications.The class of problems we consider involves the optimization of a general nonconvex factorable objective function over a feasible
openaire +1 more source
Global convergence of a descent PRP type conjugate gradient method for nonconvex optimization
Applied Numerical Mathematics, 2022Qingjie Hu
exaly
Nonconvex Lagrangian-Based Optimization: Monitoring Schemes and Global Convergence
Mathematics of Operations Research, 2018Jérôme Bolte +2 more
exaly
Industrial & Engineering Chemistry Research, 1988
Gary R. Kocis, Ignacio E. Grossmann
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Gary R. Kocis, Ignacio E. Grossmann
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Parametric approach to a class of nonconvex global optimization problems
Optimization, 1988Hoang Tuy, Phan Thien Thach
exaly
On duality bound methods for nonconvex global optimization
Journal of Global Optimization, 2006Tuy Hoang
exaly