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A general quadratic programming algorithm
 Journal of the Institute of Mathematics and its Applications 7
, 1971
"... An effective algorithm is presented for quadratic programming which is of general applicability, but which is not dependent upon the availability of a linear programming code for its implementation. It is an algorithm of exchange type, the exchanges being chosen so as to avoid the accumulation of er ..."
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Cited by 29 (0 self)
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An effective algorithm is presented for quadratic programming which is of general applicability, but which is not dependent upon the availability of a linear programming code for its implementation. It is an algorithm of exchange type, the exchanges being chosen so as to avoid the accumulation
Copositive Relaxation for General Quadratic Programming
 OPTIM. METHODS SOFTW
, 1998
"... We consider general, typically nonconvex, Quadratic Programming Problems. The Semidefinite relaxation proposed by Shor provides bounds on the optimal solution, but it does not always provide sufficiently strong bounds if linear constraints are also involved. To get rid of the linear sideconstraint ..."
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Cited by 22 (2 self)
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We consider general, typically nonconvex, Quadratic Programming Problems. The Semidefinite relaxation proposed by Shor provides bounds on the optimal solution, but it does not always provide sufficiently strong bounds if linear constraints are also involved. To get rid of the linear side
Methods for Convex and General Quadratic Programming
, 2010
"... Computational methods are considered for finding a point that satisfies the secondorder necessary conditions for a general (possibly nonconvex) quadratic program (QP). The first part of the paper defines a framework for the formulation and analysis of feasiblepoint activeset methods for QP. This f ..."
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Cited by 3 (0 self)
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Computational methods are considered for finding a point that satisfies the secondorder necessary conditions for a general (possibly nonconvex) quadratic program (QP). The first part of the paper defines a framework for the formulation and analysis of feasiblepoint activeset methods for QP
Methods for Convex and General Quadratic Programming ∗
, 2013
"... Computational methods are considered for finding a point that satisfies the secondorder necessary conditions for a general (possibly nonconvex) quadratic program (QP). The first part of the paper considers the formulation and analysis of an activeset method for a generic QP with both equality and i ..."
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Computational methods are considered for finding a point that satisfies the secondorder necessary conditions for a general (possibly nonconvex) quadratic program (QP). The first part of the paper considers the formulation and analysis of an activeset method for a generic QP with both equality
A Global Optimization Algorithm for Generalized Quadratic Programming
"... We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints. By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence of relaxa ..."
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We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints. By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence
Inertiacontrolling methods for general quadratic programming
 SIAM Review
, 1991
"... Abstract. Activeset quadratic programming (QP) methods use a working set to define the search direction and multiplier estimates. In the method proposed by Fletcher in 1971, and in several subsequent mathematically equivalent methods, the working set is chosen to control the inertia of the reduced ..."
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Cited by 38 (3 self)
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Abstract. Activeset quadratic programming (QP) methods use a working set to define the search direction and multiplier estimates. In the method proposed by Fletcher in 1971, and in several subsequent mathematically equivalent methods, the working set is chosen to control the inertia of the reduced
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 628 (1 self)
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learning tasks with many training examples, offtheshelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents
Making LargeScale SVM Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 1861 (17 self)
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learning tasks with many training examples, offtheshelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SV M light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic
GLOBAL AND FINITE TERMINATION OF A TWOPHASE AUGMENTED LAGRANGIAN FILTER METHOD FOR GENERAL QUADRATIC PROGRAMS
, 2007
"... We present a twophase algorithm for solving largescale quadratic programs (QPs). In the first phase, gradientprojection iterations approximately minimize a boundconstrained augmented Lagrangian function and provide an estimate of the optimal active set. In the second phase, an equalityconstrai ..."
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Cited by 1 (0 self)
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We present a twophase algorithm for solving largescale quadratic programs (QPs). In the first phase, gradientprojection iterations approximately minimize a boundconstrained augmented Lagrangian function and provide an estimate of the optimal active set. In the second phase, an equality
Results 1  10
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54,180