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Results 1 - 10
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27,419
Linear Equality Constraints and Homomorphous Mappings in PSO
"... We present a homomorphous mapping that converts problems with linear equality constraints into fully unconstrained and lower-dimensional 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 lower-dimensional problems for optimization with PSO. This approach, in contrast with feasibility preservation methods, allows any unconstrained optimization algorithm
Multi-Frame Blind Deconvolution With Linear Equality Constraints
- Proc. SPIE 2002, 4792, 146-155. - 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 MFBD--LEC 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 MFBD--LEC 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 3 (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 two-sided 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 two-sided 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
- SIAM J. OPTIMIZATION
, 2010
"... 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 of problem ..."
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Cited by 6 (1 self)
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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 Primal-Dual Algorithm for Minimizing a Non-Convex Function Subject to Bound and Linear Equality Constraints
, 1996
"... A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primal-dual step and a Newton-like step in order to ensure descent on a suitable merit function. Converge ..."
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Cited by 17 (0 self)
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A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primal-dual step and a Newton-like step in order to ensure descent on a suitable merit function
High-dimensionality 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 high-dimensional 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 high-dimensional 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
A Primal-Dual Trust-Region Algorithm for Minimizing a Non-Convex Function Subject to General Inequality and Linear Equality Constraints
, 1999
"... A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trustregion model to ensure descent on a suitable merit function. Convergence is proved to second-order critical ..."
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Cited by 6 (0 self)
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A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trustregion model to ensure descent on a suitable merit function. Convergence is proved to second
Results 1 - 10
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27,419
