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Linear Equality Constraints and Homomorphous Mappings in PSO
"... We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lowerdimensional problems for optimization with PSO. This approach, in contrast with feasibility preservation methods, allows any unconstrained optimization algorithm to be applied ..."
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We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lowerdimensional problems for optimization with PSO. This approach, in contrast with feasibility preservation methods, allows any unconstrained optimization algorithm
Linear Equality Constraints and Homomorphous Mappings in PSO
"... Abstract We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lowerdimensional problems for optimization with PSO. This approach, in contrast with feasibility preservation methods, allows any unconstrained optimization algorithm to ..."
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Abstract We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lowerdimensional problems for optimization with PSO. This approach, in contrast with feasibility preservation methods, allows any unconstrained optimization algorithm
MultiFrame Blind Deconvolution With Linear Equality Constraints
 Proc. SPIE 2002, 4792, 146155.  12  Intensity 1.0 0.8 0.6 0.4 0.2 0.0 10 5 0 5 10 Position in Image Plane
, 2002
"... The Phase Diverse Speckle (PDS) problem is formulated mathematically as Multi Frame Blind Deconvolution (MFBD) together with a set of Linear Equality Constraints (LECs) on the wavefront expansion parameters. This MFBDLEC formulation is quite general and, in addition to PDS, it allows the same code ..."
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Cited by 7 (2 self)
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The Phase Diverse Speckle (PDS) problem is formulated mathematically as Multi Frame Blind Deconvolution (MFBD) together with a set of Linear Equality Constraints (LECs) on the wavefront expansion parameters. This MFBDLEC formulation is quite general and, in addition to PDS, it allows the same
On the Weighting Method for Least Squares Problems with Linear Equality Constraints
, 1997
"... The weighting method for solving a least squares problem with linear equality constraints multiplies the constraints by a large number and appends them to the top of the least squares problem, which is then solved by standard techniques. In this paper we give a new analysis of the method, based on t ..."
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Cited by 2 (0 self)
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The weighting method for solving a least squares problem with linear equality constraints multiplies the constraints by a large number and appends them to the top of the least squares problem, which is then solved by standard techniques. In this paper we give a new analysis of the method, based
A Newton Barrier method for Minimizing a Sum of Euclidean Norms subject to linear equality constraints
, 1995
"... An algorithm for minimizing a sum of Euclidean Norms subject to linear equality constraints is described. The algorithm is based on a recently developed Newton barrier method for the unconstrained minimization of a sum of Euclidean norms (MSN ). The linear equality constraints are handled using an e ..."
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Cited by 27 (2 self)
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An algorithm for minimizing a sum of Euclidean Norms subject to linear equality constraints is described. The algorithm is based on a recently developed Newton barrier method for the unconstrained minimization of a sum of Euclidean norms (MSN ). The linear equality constraints are handled using
AN EFFICIENT METHOD FOR MINIMIZING A CONVEX SEPARABLE LOGARITHMIC FUNCTION SUBJECT TO A CONVEX INEQUALITY CONSTRAINT OR LINEAR EQUALITY CONSTRAINT
, 2005
"... We consider the problem of minimizing a convex separable logarithmic function over a region defined by a convex inequality constraint or linear equality constraint, and twosided bounds on the variables (box constraints). Such problems are interesting from both theoretical and practical point of vie ..."
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Cited by 1 (0 self)
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We consider the problem of minimizing a convex separable logarithmic function over a region defined by a convex inequality constraint or linear equality constraint, and twosided bounds on the variables (box constraints). Such problems are interesting from both theoretical and practical point
An optimal algorithm for minimization of quadratic functions with bounded spectrum subject to separable convex inequality and linear equality constraints, acceptted in
 SIAM J. Optimization
, 2010
"... Abstract. An, in a sense, optimal algorithm for minimization of quadratic functions subject to separable convex inequality and linear equality constraints is presented. Its unique feature is an error bound in terms of bounds on the spectrum of the Hessian of the cost function. If applied to a class ..."
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Cited by 2 (0 self)
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Abstract. An, in a sense, optimal algorithm for minimization of quadratic functions subject to separable convex inequality and linear equality constraints is presented. Its unique feature is an error bound in terms of bounds on the spectrum of the Hessian of the cost function. If applied to a class
A PrimalDual Algorithm for Minimizing a NonConvex Function Subject to Bound and Linear Equality Constraints
, 1996
"... A new primaldual algorithm is proposed for the minimization of nonconvex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primaldual step and a Newtonlike step in order to ensure descent on a suitable merit function. Converge ..."
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Cited by 16 (0 self)
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A new primaldual algorithm is proposed for the minimization of nonconvex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primaldual step and a Newtonlike step in order to ensure descent on a suitable merit function
Highdimensionality effects in the Markowitz problem and other quadratic programs with linear equality constraints: risk underestimation
"... We study the properties of solutions of quadratic programs with linear equality constraints whose parameters are estimated from data in the highdimensional setting where p, the number of variables in the problem, is of the same order of magnitude as n, the number of observations used to estimate th ..."
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Cited by 11 (2 self)
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We study the properties of solutions of quadratic programs with linear equality constraints whose parameters are estimated from data in the highdimensional setting where p, the number of variables in the problem, is of the same order of magnitude as n, the number of observations used to estimate
Results 1  10
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2,691,255