Results 121 to 130 of about 3,006 (163)

Lifted Inference for Convex Quadratic Programs

Proceedings of the AAAI Conference on Artificial Intelligence, 2017
Symmetry is the essential element of lifted inferencethat has recently demonstrated the possibility to perform very efficient inference in highly-connected, but symmetric probabilistic models. This raises the question, whether this holds for optimization problems in general.Here we show that for a large classof optimization methods this
Martin Mladenov, Leonard Kleinhans, Kristian Kersting   +2 more
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Convex Quadratic Programming Approach

Journal of Global Optimization, 2001
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Convex Relaxations of (0, 1)-Quadratic Programming

Mathematics of Operations Research, 1995
We consider three parametric relaxations of the (0, l)-quadratic programming problem. These relaxations are to: quadratic maximization over simple box constraints, quadratic maximization over the sphere, and the maximum eigenvalue of a bordered matrix.
Svatopluk Poljak, Henry Wolkowicz
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Quadratic convex reformulations for quadratic 0–1 programming

4OR, 2007
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Conic approximation to nonconvex quadratic programming with convex quadratic constraints

Journal of Global Optimization, 2014
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Zhibin Deng, Shu-Cherng Fang, Qingwei Jin, Cheng Lu 0007   +3 more
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Solution existence and stability of quadratically constrained convex quadratic programs

Optimization Letters, 2011
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Do Sang Kim, Nguyen Nang Tam, Nguyen Dong Yen   +2 more
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Hidden convexity in some nonconvex quadratically constrained quadratic programming

Mathematical Programming, 1996
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Aharon Ben-Tal, Marc Teboulle
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Quadratic convex reformulations for multiObjective binary quadratic programming

Journal of Global Optimization
Multiobjective binary quadratic programming refers to optimization problems involving multiple quadratic-potentially non-convex-objective functions and a feasible set that includes binary constraints on the variables. In this paper, we extend the well-established Quadratic Convex Reformulation technique, originally developed for single-objective binary
De Santis M., Letocart L., Zhang Y.
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Convex and Quadratic Programming

1994
With the exception of Section 3.2, this book is entirely devoted to a single method of solving nonlinear programming problems, namely the linearization method. In this, it differs from most books on this subject, which usually consider various methods. The various algorithms and approaches described in the literature are not random.
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On improving convex quadratic programming relaxation for the quadratic assignment problem

Journal of Combinatorial Optimization, 2013
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Yong Xia 0002, Wajeb Gharibi
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