Results 1  10
of
1,471
Some optimal inapproximability results
, 2002
"... We prove optimal, up to an arbitrary ffl? 0, inapproximability results for MaxEkSat for k * 3, maximizing the number of satisfied linear equations in an overdetermined system of linear equations modulo a prime p and Set Splitting. As a consequence of these results we get improved lower bounds for ..."
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Cited by 751 (11 self)
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We prove optimal, up to an arbitrary ffl? 0, inapproximability results for MaxEkSat for k * 3, maximizing the number of satisfied linear equations in an overdetermined system of linear equations modulo a prime p and Set Splitting. As a consequence of these results we get improved lower bounds
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 819 (28 self)
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likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sumproduct, cluster variational methods, expectationpropagation, mean field methods, maxproduct and linear programming relaxation, as well as conic programming relaxations — can
Least angle regression
, 2004
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
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Cited by 1326 (37 self)
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The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope
Sparse Reconstruction by Separable Approximation
, 2007
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing ..."
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Cited by 373 (38 self)
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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing
NonLinear Approximation of Reflectance Functions
, 1997
"... We introduce a new class of primitive functions with nonlinear parameters for representing light reflectance functions. The functions are reciprocal, energyconserving and expressive. They can capture important phenomena such as offspecular reflection, increasing reflectance and retroreflection. ..."
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Cited by 269 (10 self)
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We introduce a new class of primitive functions with nonlinear parameters for representing light reflectance functions. The functions are reciprocal, energyconserving and expressive. They can capture important phenomena such as offspecular reflection, increasing reflectance and retro
A Rank Minimization Heuristic with Application to Minimum Order System Approximation
, 2001
"... Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) constraints. Except in some special cases, solving this rank minimization probiem (globally) is ve ..."
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Cited by 274 (10 self)
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Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) constraints. Except in some special cases, solving this rank minimization probiem (globally
An approximation algorithm for the generalized assignment problem
, 1993
"... The generalized assignment problem can be viewed as the following problem of scheduling parallel machines with costs. Each job is to be processed by exactly one machine; processing job j on machine i requires time pif and incurs a cost of c,f, each machine / is available for 7", time units, ..."
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Cited by 200 (5 self)
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approximation algorithm to minimize a weighted sum of the cost and the makespan, i.e., the maximum job completion time. We also consider the objective of minimizing the mean job completion time. We show that there is a polynomialtime algorithm that, given values M and 7", either proves
Using linear programming to decode binary linear codes
 IEEE TRANS. INFORM. THEORY
, 2005
"... A new method is given for performing approximate maximumlikelihood (ML) decoding of an arbitrary binary linear code based on observations received from any discrete memoryless symmetric channel. The decoding algorithm is based on a linear programming (LP) relaxation that is defined by a factor grap ..."
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Cited by 183 (9 self)
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A new method is given for performing approximate maximumlikelihood (ML) decoding of an arbitrary binary linear code based on observations received from any discrete memoryless symmetric channel. The decoding algorithm is based on a linear programming (LP) relaxation that is defined by a factor
Linear Approximations of Addition Modulo 2 n1 ⋆
"... Abstract. Addition modulo 2 31 − 1 is a basic arithmetic operation in the stream cipher ZUC. For evaluating ZUC in resistance to linear cryptanalysis, it is necessary to study properties of linear approximations of the addition modulo 2 31 − 1. In this paper we discuss linear approximations of the a ..."
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Abstract. Addition modulo 2 31 − 1 is a basic arithmetic operation in the stream cipher ZUC. For evaluating ZUC in resistance to linear cryptanalysis, it is necessary to study properties of linear approximations of the addition modulo 2 31 − 1. In this paper we discuss linear approximations
On Approximation of Functions by Exponential Sums
, 2005
"... We introduce a new approach, and associated algorithms, for the efficient approximation of functions and sequences by short linear combinations of exponential functions with complexvalued exponents and coefficients. These approximations are obtained for a finite but arbitrary accuracy and typically ..."
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Cited by 40 (7 self)
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We introduce a new approach, and associated algorithms, for the efficient approximation of functions and sequences by short linear combinations of exponential functions with complexvalued exponents and coefficients. These approximations are obtained for a finite but arbitrary accuracy
Results 1  10
of
1,471