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Adaptive Constraint Reduction for Convex Quadratic Programming and Training Support Vector Machines
, 2008
"... Convex quadratic programming (CQP) is an optimization problem of minimizing a convex quadratic objective function subject to linear constraints. We propose an adaptive constraint reduction primaldual interiorpoint algorithm for convex quadratic programming with many more constraints than variables ..."
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Cited by 4 (2 self)
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Convex quadratic programming (CQP) is an optimization problem of minimizing a convex quadratic objective function subject to linear constraints. We propose an adaptive constraint reduction primaldual interiorpoint algorithm for convex quadratic programming with many more constraints than
ABSTRACT Title of dissertation: Adaptive Constraint Reduction for Convex Quadratic Programming and Training Support Vector Machines
, 2008
"... Convex quadratic programming (CQP) is an optimization problem of minimizing a convex quadratic objective function subject to linear constraints. We propose an adaptive constraint reduction primaldual interiorpoint algorithm for convex quadratic programming with many more constraints than variabl ..."
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Convex quadratic programming (CQP) is an optimization problem of minimizing a convex quadratic objective function subject to linear constraints. We propose an adaptive constraint reduction primaldual interiorpoint algorithm for convex quadratic programming with many more constraints than
Convex Analysis
, 1970
"... In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a lo ..."
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Cited by 5350 (67 self)
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long time, ‘variational ’ problems have been identified mostly with the ‘calculus of variations’. In that venerable subject, built around the minimization of integral functionals, constraints were relatively simple and much of the focus was on infinitedimensional function spaces. A major theme
Constraint Logic Programming: A Survey
"... Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in differe ..."
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Cited by 864 (25 self)
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Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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. This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. This approach is powerful because the optimization can be completed in polynomial time with standard scientific software. The paper provides general conditions which ensure
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
, 2008
"... ..."
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
A Survey of Program Slicing Techniques
 JOURNAL OF PROGRAMMING LANGUAGES
, 1995
"... A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser in 197 ..."
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Cited by 777 (8 self)
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A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser
Abduction in Logic Programming
"... Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over th ..."
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Cited by 616 (76 self)
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Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over
Do investmentcash flow sensitivities provide useful measures of financing constraints?
 QUARTERLY JOURNAL OF ECONOMICS
, 1997
"... No. This paper investigates the relationship between financing constraints and investmentcash flow sensitivities by analyzing the firms identified by Fazzari, Hubbard, and Petersen as having unusually high investmentcash flow sensitivities. We Quarterlynd that firms that appear less Quarterlynanci ..."
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Cited by 656 (8 self)
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No. This paper investigates the relationship between financing constraints and investmentcash flow sensitivities by analyzing the firms identified by Fazzari, Hubbard, and Petersen as having unusually high investmentcash flow sensitivities. We Quarterlynd that firms that appear less
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436,496