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2,039,726
Approximating Parameterized Convex Optimization Problems ∗
, 2010
"... We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an ε-approximate solution (and a corresponding ε-coreset) along the entire parameter path. We prove correctness and parameterized optim ..."
Abstract
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Cited by 10 (4 self)
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We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an ε-approximate solution (and a corresponding ε-coreset) along the entire parameter path. We prove correctness and parameterized
Residual selection model for convex optimization problems
"... We present residual selection method for convex optimization problems, in particular for convex feasibility problems. We show that the residual selection method is a special case of the surrogate constraints method. We also present ..."
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We present residual selection method for convex optimization problems, in particular for convex feasibility problems. We show that the residual selection method is a special case of the surrogate constraints method. We also present
Convex Optimization Problems Involving Finite autocorrelation sequences
, 2001
"... We discuss convex optimization problems where some of the variables are constrained to be finite autocorrelation sequences. Problems of this form arise in signal processing and communications, and we describe applications in filter design and system identification. Autocorrelation constraints in opt ..."
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Cited by 40 (0 self)
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We discuss convex optimization problems where some of the variables are constrained to be finite autocorrelation sequences. Problems of this form arise in signal processing and communications, and we describe applications in filter design and system identification. Autocorrelation constraints
Elementary Algorithms for Solving Convex Optimization Problems
"... The rapid growth in data availability has led to modern large scale convex optimization problems that pose new practical and theoretical challenges. Examples include classifica-tion problems such as customer segmentation in retail and credit scoring in insurance. Classical optimization and machine l ..."
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The rapid growth in data availability has led to modern large scale convex optimization problems that pose new practical and theoretical challenges. Examples include classifica-tion problems such as customer segmentation in retail and credit scoring in insurance. Classical optimization and machine
SEPARABLE CONVEX OPTIMIZATION PROBLEMS WITH LINEAR ASCENDING CONSTRAINTS
- SUBMITTED TO THE SIAM JOURNAL ON OPTIMIZATION, JUL. 2007.
, 2007
"... Separable convex optimization problems with linear ascending inequality and equality constraints are addressed in this paper. Under an ordering condition on the slopes of the functions at the origin, an algorithm that determines the optimum point in a finite number of steps is described. The optimu ..."
Abstract
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Cited by 8 (4 self)
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Separable convex optimization problems with linear ascending inequality and equality constraints are addressed in this paper. Under an ordering condition on the slopes of the functions at the origin, an algorithm that determines the optimum point in a finite number of steps is described
Convex optimization problems involving ¯- nite autocorrelation sequences
, 2000
"... We discuss convex optimization problems where some of the variables are constrained to be ¯nite autocorrelation sequences. Problems of this form arise in signal processing and communications, and we describe applications in ¯lter design and system identi¯cation. Autocorrelation constraints in optimi ..."
Abstract
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Cited by 1 (0 self)
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We discuss convex optimization problems where some of the variables are constrained to be ¯nite autocorrelation sequences. Problems of this form arise in signal processing and communications, and we describe applications in ¯lter design and system identi¯cation. Autocorrelation constraints
On The Generic Properties Of Convex Optimization Problems In Conic Form
, 1997
"... We prove that strict complementarity, primal and dual nondegeneracy of optimal solutions of convex optimization problems in conic form are generic properties. In this paper, we say generic to mean that the set of data possessing the desired property (or properties) has the same Hausdorff measure as ..."
Abstract
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Cited by 22 (3 self)
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We prove that strict complementarity, primal and dual nondegeneracy of optimal solutions of convex optimization problems in conic form are generic properties. In this paper, we say generic to mean that the set of data possessing the desired property (or properties) has the same Hausdorff measure
A new constraint qualification and conjugate duality for composed convex optimization problems
- J. Optimization Theory Appl
, 2004
"... Abstract We present a new constraint qualification which guarantees strong duality between a cone-constrained convex optimization problem and its Fenchel-Lagrange dual. This result is applied to a convex optimization problem having, for a given nonempty convex cone K, as objective function a K-conve ..."
Abstract
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Cited by 8 (4 self)
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Abstract We present a new constraint qualification which guarantees strong duality between a cone-constrained convex optimization problem and its Fenchel-Lagrange dual. This result is applied to a convex optimization problem having, for a given nonempty convex cone K, as objective function a K-convex
Global and uniform convergence of subspace correction methods for some convex optimization problems
- Math. Comp
, 2001
"... Abstract. This paper gives some global and uniform convergence estimates for a class of subspace correction (based on space decomposition) iterative methods applied to some unconstrained convex optimization problems. Some multigrid and domain decomposition methods are also discussed as special examp ..."
Abstract
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Cited by 31 (10 self)
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Abstract. This paper gives some global and uniform convergence estimates for a class of subspace correction (based on space decomposition) iterative methods applied to some unconstrained convex optimization problems. Some multigrid and domain decomposition methods are also discussed as special
Results 1 - 10
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2,039,726
