Suchergebnisse - convex linearly-constrained quadratic programming

New variants of the criss-cross method for linearly constrained convex quadratic programming von Akkeleş, Arif A., Balogh, László, Illés, Tibor

“… (with LIFO and most-often-selected-variable pivot rules) are generalized for linearly constrained convex primal …”
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Sparsity-Aware Noise Subspace Fitting for DOA Estimation von Zheng, Chundi, Chen, Huihui, Wang, Aiguo

“… Our formulation leads to a convex linearly constrained quadratic programming (LCQP) problem that enjoys global convergence without the need of accurate initialization and can be easily solved by existing LCQP solvers …”
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Finiteness of the quadratic primal simplex method when s-monotone index selection rules are applied von Csizmadia, Adrienn, Csizmadia, Zsolt, Illés, Tibor

“… This paper considers the primal quadratic simplex method for linearly constrained convex quadratic programming problems …”
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A successive domain-reduction scheme for linearly constrained quadratic integer programming problems von Wang, Fenlan

“… A new exact solution method is developed in this paper for solving nonseparable linearly constrained quadratic integer programming problems with convex, concave or indefinite objective functions …”
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Convergence analysis of generalized ADMM with majorization for linearly constrained composite convex optimization von Li, Hongwu, Zhang, Haibin, Xiao, Yunhai, Li, Peili

“… The generalized alternating direction method of multipliers (ADMM) of Xiao et al. (Math Prog Comput 10:533–555, 2018) aims at the two-block linearly constrained composite convex programming problem, in which each block is in the …”
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Interior algorithms for linear, quadratic, and linearly constrained convex programming von Ye, Yinyu

“… : convex quadratic and linearly constrained convex programs. These algorithms are polynomial-time algorithms if the objective function is convex quadratic …”
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An interior point-proximal method of multipliers for convex quadratic programming von Pougkakiotis, Spyridon, Gondzio, Jacek

“… ). The resulting algorithm (IP-PMM) is interpreted as a primal-dual regularized IPM, suitable for solving linearly constrained convex quadratic programming problems …”
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An Inexact Accelerated Proximal Gradient Method for Large Scale Linearly Constrained Convex SDP von Jiang, Kaifeng, Sun, Defeng, Toh, Kim-Chuan

“… The accelerated proximal gradient (APG) method, first proposed by Nesterov for minimizing smooth convex functions, later extended by Beck and Teboulle to composite convex objective functions, and studied in a unifying manner by Tseng …”
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Inverse optimization for linearly constrained convex separable programming problems von Zhang, Jianzhong, Xu, Chengxian

“… In this paper, we study inverse optimization for linearly constrained convex separable programming problems that have wide applications in industrial and managerial areas …”
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On the Convergence Properties of a Majorized Alternating Direction Method of Multipliers for Linearly Constrained Convex Optimization Problems with Coupled Objective Functions von Cui, Ying, Li, Xudong, Sun, Defeng, Toh, Kim-Chuan

“… In this paper, we establish the convergence properties for a majorized alternating direction method of multipliers for linearly constrained convex optimization problems, whose objectives contain coupled functions …”
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The Boosted DC Algorithm for Linearly Constrained DC Programming von Aragón-Artacho, F. J., Campoy, R., Vuong, P. T.

“… The Boosted Difference of Convex functions Algorithm (BDCA) has been recently introduced to accelerate the performance of the classical Difference of Convex functions Algorithm (DCA …”
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An efficient adaptive accelerated inexact proximal point method for solving linearly constrained nonconvex composite problems von Kong, Weiwei, Melo, Jefferson G., Monteiro, Renato D. C.

“… This paper proposes an efficient adaptive variant of a quadratic penalty accelerated inexact proximal point (QP-AIPP …”
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A primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming von Wang, Chengjing, Tang, Peipei

“… In this paper, we propose a primal majorized semismooth Newton-CG augmented Lagrangian method for large-scale linearly constrained convex programming problems, especially for some difficult problems …”
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Primal-Dual Relationship Between Levenberg–Marquardt and Central Trajectories for Linearly Constrained Convex Optimization von Behling, Roger, Gonzaga, Clovis, Haeser, Gabriel

“… –Marquardt trajectory and eventually moves to the central path. Our main theorem is particularly relevant in quadratic programming, where points on the primal-dual Levenberg …”
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Fast algorithm for singly linearly constrained quadratic programs with box-like constraints von Liu, Meijiao, Liu, Yong-Jin

“… This paper focuses on a singly linearly constrained class of convex quadratic programs with box-like constraints …”
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A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training von Tseng, Paul, Yun, Sangwoon

“… ) with bound constraints and a single linear equality constraint. We propose a (block) coordinate gradient descent method for solving this problem and, more generally, linearly constrained smooth optimization …”
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A quadratically convergent semismooth Newton method for nonlinear semidefinite programming without generalized Jacobian regularity von Feng, Fuxiaoyue, Ding, Chao, Li, Xudong

“… We introduce a quadratically convergent semismooth Newton method for nonlinear semidefinite programming that eliminates the need for the generalized Jacobian regularity, a common yet stringent …”
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Preconditioning Indefinite Systems in Interior Point Methods for Optimization von Bergamaschi, Luca, Gondzio, Jacek, Zilli, Giovanni

“… Every Newton step in an interior-point method for optimization requires a solution of a symmetric indefinite system of linear equations. Most of today's codes …”
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Feature matching using quasi-conformal maps von Wang, Chun-xue, Liu, Li-gang

“… We present a fully automatic method for finding geometrically consistent correspondences while discarding outliers from the candidate point matches in two …”
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A fast global algorithm for singly linearly constrained separable binary quadratic program with partially identical parameters von Lu, Cheng, Wu, Junhao, Deng, Zhibin, Li, Shaoze

“… The singly linearly constrained separable binary quadratic programming problem (SLSBQP) has a wide variety of applications in practice …”
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