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Convex Quadratic Programming for Object Localization
"... 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 ..."
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a convex quadratic programming (CQP) relaxation method to solve the problem more robustly. The CQP relaxation uses a small number of basis points to represent the target point space and therefore can be used in very large scale matching problems. We further propose a successive convexification
Further Development on the Interior Algorithm for Convex Quadratic Programming
 Dept. of EngineeringEconomic Systems, Stanford University
, 1987
"... The interior trust region algorithm for convex quadratic programming is further developed. This development is motivated by the barrier function and the "center" pathfollowing methods, which create a sequence of primal and dual interior feasible points converging to the optimal solution. ..."
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Cited by 9 (2 self)
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The interior trust region algorithm for convex quadratic programming is further developed. This development is motivated by the barrier function and the "center" pathfollowing methods, which create a sequence of primal and dual interior feasible points converging to the optimal solution
Parallel Constraint Distribution in Convex Quadratic Programming
 Computer Sciences Department, University of Wisconsin
, 1992
"... . We consider convex quadratic programs with large numbers of constraints. We distribute these constraints among several parallel processors and modify the objective function for each of these subproblems with Lagrange multiplier information from the other processors. New Lagrange multiplier informa ..."
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Cited by 3 (1 self)
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. We consider convex quadratic programs with large numbers of constraints. We distribute these constraints among several parallel processors and modify the objective function for each of these subproblems with Lagrange multiplier information from the other processors. New Lagrange multiplier
A New Heuristic for the Convex Quadratic Programming Problem
, 2015
"... This paper presents a new heuristic to linearise the convex quadratic programming problem. The usual KarushKuhnTucker conditions are used but in this case a linear objective function is also formulated from the set of linear equations and complementarity slackness conditions. An unboundedness cha ..."
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This paper presents a new heuristic to linearise the convex quadratic programming problem. The usual KarushKuhnTucker conditions are used but in this case a linear objective function is also formulated from the set of linear equations and complementarity slackness conditions. An un
Convex Quadratic Programming Relaxations for Network Scheduling Problems
 Algorithms  ESA '99
, 1999
"... . In network scheduling a set of jobs must be scheduled on unrelated parallel processors or machines which are connected by a network. Initially, each job is located on some machine in the network and cannot be started on another machine until sufficient time elapses to allow the job to be transmitt ..."
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Cited by 4 (1 self)
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completion time. The main contribution of this paper is a provably good convex quadratic programming relaxation of strongly polynomial size for this problem. Until now, only linear programming relaxations in time or intervalindexed variables have been studied. Those LP relaxations, however, suffer from a
Consider the following convex quadratic programming problem
, 2013
"... Copyright © 2014 Ruopeng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accordance of the Creative Commons Attributi ..."
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Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intellectual property Ruopeng Wang et al. All Copyright © 2014 are guarded by law and by SCIRP as a guardian. The present paper is devoted to a novel smoothing function method for convex quadratic programming problem
QPSchur: A dual, activeset, Schurcomplement method for largescale and structured convex quadratic programming. Optimization and Engineering, 7 (2006) 5–32. the results are drawn up in table 2. We constat that when m ≤ 30 our method is very efficient com
"... structured convex quadratic programming ..."
A New Bound for the Quadratic Assignment Problem Based on Convex Quadratic Programming
 MATHEMATICAL PROGRAMMING
, 1999
"... We describe a new convex quadratic programming bound for the quadratic assignment problem (QAP). The construction of the bound uses a semidefinite programming representation of a basic eigenvalue bound for QAP. The new bound dominates the wellknown projected eigenvalue bound, and appears to be comp ..."
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Cited by 37 (4 self)
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We describe a new convex quadratic programming bound for the quadratic assignment problem (QAP). The construction of the bound uses a semidefinite programming representation of a basic eigenvalue bound for QAP. The new bound dominates the wellknown projected eigenvalue bound, and appears
Some Randomized Algorithms for Convex Quadratic Programming
"... Below we adapt some randomized algorithms of Welzl [10] and Clarkson [3] for linear programming to the framework of socalled LPtype problems. This framework is quite general and allows a unified and elegant presentation and analysis. LPtype problems include minimization of a convex quadratic f ..."
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Cited by 2 (0 self)
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Below we adapt some randomized algorithms of Welzl [10] and Clarkson [3] for linear programming to the framework of socalled LPtype problems. This framework is quite general and allows a unified and elegant presentation and analysis. LPtype problems include minimization of a convex quadratic
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