Results 11 to 20 of about 40,565 (244)
On convex relaxations for quadratically constrained quadratic programming [PDF]
Mathematical Programming, 2012 A quadratically constrained (possibly non-convex) quadratic programming problem is considered. The efficiency, for this problem, of several known convex underestimating methods is analyzed. The underestimates, obtained by means of the considered methods, are ranked according to their tightness.
Kurt M Anstreicher
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Robust convex quadratically constrained programs [PDF]
Mathematical Programming, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Goldfarb, D., Iyengar, G.
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A compact variant of the QCR method for quadratically constrained quadratic 0-1 programs [PDF]
, 2013 Quadratic Convex Reformulation (QCR) is a technique that was originally proposed for quadratic 0-1 programs, and then extended to various other problems.
Galli, Laura, Letchford, Adam
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The Scientific World Journal, 2013 Recently, the existed proximal gradient algorithms had been used to solve non-smooth convex optimization problems. As a special nonsmooth convex problem, the singly linearly constrained quadratic programs with box constraints appear in a wide range of ...
Congying Han +3 more
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An efficient beamforming design for multipair full-duplex relaying systems
ICT Express, 2017 We consider a decode-and-forward full-duplex relaying system for multiple pairs of users. Our objective is to maximize the minimum achievable rate for all user pairs under the transmit power constraints.
Hyeon Min Kim +2 more
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Ellipsoid Bounds for Convex Quadratic Integer Programming
SIAM Journal on Optimization, 2015 Summary: Solving convex quadratic integer minimization problems by a branch-and-bound algorithm requires tight lower bounds on the optimal objective value. To obtain such dual bounds, we follow the approach of \textit{C. Buchheim} et al. [Math. Program. 135, No.
Buchheim, Christoph +2 more
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Journal of the Mechanical Behavior of Materials, 2015 This paper introduces constructing convex-relaxed programs for nonconvex optimization problems. Branch-and-bound algorithms are convex-relaxation-based techniques.
Keller André A.
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Hessian barrier algorithms for linearly constrained optimization problems [PDF]
, 2018 In this paper, we propose an interior-point method for linearly constrained optimization problems (possibly nonconvex). The method - which we call the Hessian barrier algorithm (HBA) - combines a forward Euler discretization of Hessian Riemannian ...
Bomze, Immanuel M. +3 more
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Expanding the reach of quantum optimization with fermionic embeddings [PDF]
QuantumQuadratic programming over orthogonal matrices encompasses a broad class of hard optimization problems that do not have an efficient quantum representation.
Andrew Zhao, Nicholas C. Rubin
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A distributed algorithm for high-dimension convex quadratically constrained quadratic programs [PDF]
Computational Optimization and Applications, 2021 We propose a Jacobi-style distributed algorithm to solve convex, quadratically constrained quadratic programs (QCQPs), which arise from a broad range of applications. While small to medium-sized convex QCQPs can be solved efficiently by interior-point algorithms, large-scale problems pose significant challenges to traditional algorithms that are mainly
Run Chen, Andrew L. Liu
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