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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   +2 more
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Convex Quadratic Programming for Object Localization

18th International Conference on Pattern Recognition (ICPR'06), 2006
We set out an object localization scheme based on a convex programming matching method. The proposed approach is designed to match general objects, especially objects with very little texture, and in strong background clutter; traditional methods have great difficulty in such situations. We propose a convex quadratic programming (CQP) relaxation method
null Hao Jiang   +2 more
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Maximizing perturbation radii for robust convex quadratically constrained quadratic programs

European Journal of Operational Research, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu, Pengfei, Gao, Ruotian, Xing, Wenxun
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Image segmentation by convex quadratic programming

2008 19th International Conference on Pattern Recognition, 2008
A quadratic programming formulation for multiclass image segmentation is investigated. It is proved that, in the convex case, the non-negativity constraint on the recent reported quadratic Markov measure field model can be neglected and the solution preserves the probability measure property. This allows one to design efficient optimization algorithms.
Mariano Rivera, Oscar Dalmau, Josue Tago
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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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Some Randomized Algorithms for Convex Quadratic Programming

Applied Mathematics and Optimization, 1999
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Convex Quadratic Programming in Scheduling

2015
We consider the optimization problem of scheduling a given set of jobs on unrelated parallel machines with total weighted completion time objective. This is a classical scheduling problem known to be NP-hard since the 1970s. We give a new and simplified version of the currently best-known approximation algorithm, which dates back to 1998.
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Hidden convexity in some nonconvex quadratically constrained quadratic programming

Mathematical Programming, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ben-Tal, Aharon, Teboulle, Marc
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Semidefinite Programming Based Convex Relaxation for Nonconvex Quadratically Constrained Quadratic Programming

2019
In this paper, we review recent development in semidefinite programming (SDP) based convex relaxations for nonconvex quadratically constrained quadratic programming (QCQP) problems. QCQP problems have been well known as NP-hard nonconvex problems. We focus on convex relaxations of QCQP, which forms the base of global algorithms for solving QCQP.
Rujun Jiang, Duan Li
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An interior–exterior approach for convex quadratic programming

Applied Numerical Mathematics, 2012
The authors consider the following convex quadratic programming problem \[ \min\Biggl\{c^tx+{1\over 2} x^tQx: Ax= b,\,x\geq 0\Biggr\} \] and develop a polynomial time algorithm based on the use of mixed penalties methods. -- Some numerical results are given.
El Yassini, Khalid   +1 more
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