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16,516
Robust Solutions To LeastSquares Problems With Uncertain Data
, 1997
"... . We consider leastsquares problems where the coefficient matrices A; b are unknownbutbounded. We minimize the worstcase residual error using (convex) secondorder cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpret ..."
Abstract

Cited by 205 (14 self)
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. We consider leastsquares problems where the coefficient matrices A; b are unknownbutbounded. We minimize the worstcase residual error using (convex) secondorder cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can
Rank Degeneracy and Least Squares Problems
, 1976
"... This paper is concerned with least squares problems when the least squares matrix A is near a matrix that is not of full rank. A definition of numerical rank is given. It is shown that under certain conditions when A has numerical rank r there is a distinguished r dimensional subspace of the column ..."
Abstract

Cited by 56 (2 self)
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This paper is concerned with least squares problems when the least squares matrix A is near a matrix that is not of full rank. A definition of numerical rank is given. It is shown that under certain conditions when A has numerical rank r there is a distinguished r dimensional subspace of the column
A New Approach to Variable Selection in Least Squares Problems
, 1999
"... The title Lasso has been suggested by Tibshirani [7] as a colourful name for a technique of variable selection which requires the minimization of a sum of squares subject to an ll bound r; on the solution. This forces zero components in the minimizing solution for small values of r;. Thus this bo ..."
Abstract

Cited by 244 (3 self)
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The title Lasso has been suggested by Tibshirani [7] as a colourful name for a technique of variable selection which requires the minimization of a sum of squares subject to an ll bound r; on the solution. This forces zero components in the minimizing solution for small values of r;. Thus
LeastSquares Policy Iteration
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach ..."
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Cited by 462 (12 self)
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We propose a new approach to reinforcement learning for control problems which combines valuefunction approximation with linear architectures and approximate policy iteration. This new approach
On the Least Median Square Problem
, 2003
"... We consider the exact and approximate computational complexity of the multivariate LMS linear regression estimator. The LMS estimator is among the most widely used robust linear statistical estimators. Given a set of n points in IR and a parameter k, the problem is equivalent to computing the ..."
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Cited by 8 (2 self)
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We consider the exact and approximate computational complexity of the multivariate LMS linear regression estimator. The LMS estimator is among the most widely used robust linear statistical estimators. Given a set of n points in IR and a parameter k, the problem is equivalent to computing
Leastsquares Problems
"... The multilinear leastsquares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrixvector product. The MLLS is typically a largescale problem characterized by a large number of local minimizers. It originates, ..."
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Cited by 1 (1 self)
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The multilinear leastsquares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrixvector product. The MLLS is typically a largescale problem characterized by a large number of local minimizers. It originates
Valuing American options by simulation: A simple leastsquares approach
 Review of Financial Studies
, 2001
"... This article presents a simple yet powerful new approach for approximating the value of America11 options by simulation. The kcy to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable ..."
Abstract

Cited by 517 (9 self)
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This article presents a simple yet powerful new approach for approximating the value of America11 options by simulation. The kcy to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily
nonnegative least squares problems
, 2004
"... interiorpoint gradient method for largescale totally ..."
Results 1  10
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16,516