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A LargeScale Quadratic . . .
, 2008
"... Quadratic programming (QP) problems arise naturally in a variety of applications. In many cases, a good estimate of the solution may be available. It is desirable to be able to utilize such information in order to reduce the computational cost of finding the solution. Activeset methods for solving Q ..."
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Quadratic programming (QP) problems arise naturally in a variety of applications. In many cases, a good estimate of the solution may be available. It is desirable to be able to utilize such information in order to reduce the computational cost of finding the solution. Activeset methods for solving
An algorithm for largescale quadratic programming
 IMA Journal of Numerical Analysis
, 1991
"... We describe a method for solving largescale general quadratic programming problems. Our method is based upon a compendium of ideas which have their origins in sparse matrix techniques and methods for solving smaller quadratic programs. We include a discussion on resolving degeneracy, on single phas ..."
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Cited by 22 (8 self)
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We describe a method for solving largescale general quadratic programming problems. Our method is based upon a compendium of ideas which have their origins in sparse matrix techniques and methods for solving smaller quadratic programs. We include a discussion on resolving degeneracy, on single
Numerical results for SQIC: Software for largescale quadratic programming∗
, 2014
"... Supplementary material for the article “Methods for Convex and General Quadratic Programming ” is presented in this document. Table 1 lists the values of various tolerances used to obtain the numerical results. Tables 2–13 provide detailed information of the numerical results from the largescale q ..."
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Supplementary material for the article “Methods for Convex and General Quadratic Programming ” is presented in this document. Table 1 lists the values of various tolerances used to obtain the numerical results. Tables 2–13 provide detailed information of the numerical results from the largescale
An InteriorPoint Method for General LargeScale Quadratic Programming Problems
 Annals of Operations Research
, 1996
"... In this paper we present an interior point algorithm for solving both convex and nonconvex quadratic programs. The method, which is an extension of our interior point work on linear programming problems, efficiently solves a wide class of large scale problems and forms the basis for a sequential qua ..."
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Cited by 3 (0 self)
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In this paper we present an interior point algorithm for solving both convex and nonconvex quadratic programs. The method, which is an extension of our interior point work on linear programming problems, efficiently solves a wide class of large scale problems and forms the basis for a sequential
Joining Forces in Solving LargeScale Quadratic Assignment Problems in Parallel
 In Proc. of the 11th International Parallel Processing Symposium
, 1996
"... Program libraries are one way to make the cooperation between specialists from various fields successful: the separation of applicationspecific knowledge from applicationindependent tasks ensures portability, maintenance, extensibility, and flexibility. The current paper demonstrates the success ..."
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Cited by 9 (1 self)
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the success in combining problemspecific knowledge for the quadratic assignment problem (QAP) with the raw computing power offered by contemporary parallel hardware by using the library of parallel search algorithms ZRAM. Solutions of previously unsolved large standard testinstances of the QAP
to KKT systems in Interior Point methods for LargeScale Quadratic Programming problems
"... Interior Point methods for linear and nonlinear optimization problems have received an increasing attention in the last years. Main reasons for the interest in Interior Point methods are their very attractive computational efficiency and good theoretical convergence properties, and their applicabil ..."
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software is the solution of the linear system, named KKT system, that arises at each iteration of the method. Sparse direct methods for linear systems are widely used in Interior Point based software, but when dealing with largescale problems their computational cost may become prohibitive. A promising
PARALLEL MEMETIC ALGORITHM WITH SELECTIVE LOCAL SEARCH FOR LARGE SCALE QUADRATIC ASSIGNMENT PROBLEMS
, 2006
"... The extent of the application of local searches in canonical memetic algorithm is typically based on the principle of “more is better”. In the same spirit, the parallel memetic algorithm (PMA) is an important extension of the canonical memetic algorithm which applies local searches to every transit ..."
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Cited by 4 (1 self)
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problems, in particular largescale quadratic assignment problems (QAPs). A distinct feature of the PMASLS to be noted in our study is the sampling size. We make use of a normal distribution scheme to determine the sampling ratio. Empirical study on large scale QAPs with PMASLS and PMACLS are presented
Solving largescale quadratic eigenvalue problem with Hamiltonian eigenstructure using a structurepreserving Krylov subspace method
 Oxford University
, 2007
"... Abstract. We consider the numerical solution of quadratic eigenproblems with spectra that exhibit Hamiltonian symmetry. We propose to solve such problems by applying a KrylovSchurtype method based on the symplectic Lanczos process to a structured linearization of the quadratic matrix polynomial. I ..."
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Cited by 5 (3 self)
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Abstract. We consider the numerical solution of quadratic eigenproblems with spectra that exhibit Hamiltonian symmetry. We propose to solve such problems by applying a KrylovSchurtype method based on the symplectic Lanczos process to a structured linearization of the quadratic matrix polynomial
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
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