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On the Solution of Large Scale Partially Separable Unconstrained Optimization Problems Using Element-by-Element Preconditioners

by Element-by-element Preconditioners, Jérome P. Décamps, Jean-Yves L'Excellent, Michel J. Daydé, Nicholas I. M. Gould , 1996
"... We study the solution of large scale nonlinear unconstrained problems using techniques that exploit the commonly arising structure of partial separability. We show how effective preconditioners can be computed to design iterative methods exploiting partial separability. Our unconstrained optimiz ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
experiments on problems from the CUTE collection indicate the effectiveness of Element-by-Element preconditioners versus classical preconditioners. 1 Introduction We study the numerical solution of large scale unconstrained optimization problems. We consider the minimization of a partially separable

On the Use of Element-by-Element Preconditioners to Solve Large Scale Partially Separable Optimization Problems

by Michel J. Daydé, Jean-Yves L'Excellent, Nicholas I. M. Gould
"... We study the solution of large-scale nonlinear optimization problems by methods which aim to exploit their inherent structure. In particular, we consider the all-pervasive property of partial separability, first studied by Griewank and Toint (1982b). A typical minimizationmethod for nonlinear optimi ..."
Abstract - Cited by 8 (5 self) - Add to MetaCart
. Numerical experiments indicate the effectiveness of these preconditioners on large-scale examples. Keywords: large-scale problems, unconstrained optimization, elememt-by-element preconditioners, conjugate-gradients. AMS(MOS) subject classifications: 65F05, 65F10, 65F15, 65F50, 65K05, 90C30. Also appeared

Solution Of Structured Systems Of Linear Equations Using Element-By-Element Preconditioners

by M.J. Daydé, J.Y. L'Excellent, N.I.M. Gould , 1995
"... We consider the solution of the n by n system of linear equations, Ax = b, where A is both sparse and structured so that it may be expressed as A = p X i=1 A i : Sparse structured linear systems arise in many applications. The elementary matrices A i are of low rank, and are usually sparse s ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
so that their nonzero entries may be represented as a small, dense block. We assume that A is a large and normally positive definite symmetric matrix. The solution technique considered is the conjugate gradient method using a range of Element-By-Element (EBE) preconditioners that were introduced

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

by Philip E. Gill, Walter Murray, Michael A. Saunders , 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract - Cited by 582 (23 self) - Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first

Separation of ownership and control

by Eugene F. Fama, Michael C. Jensen - JOURNAL OF LAW AND ECONOMICS , 1983
"... This paper analyzes the survival of organizations in which decision agents do not bear a major share of the wealth effects of their decisions. This is what the literature on large corporations calls separation of “ownership” and “control.” Such separation of decision and risk bearing functio ..."
Abstract - Cited by 1564 (7 self) - Add to MetaCart
This paper analyzes the survival of organizations in which decision agents do not bear a major share of the wealth effects of their decisions. This is what the literature on large corporations calls separation of “ownership” and “control.” Such separation of decision and risk bearing

N Degrees of Separation: Multi-Dimensional Separation of Concerns

by Peri Tarr, Harold Ossher, William Harrison, Stanley M. Sutton, Jr. - IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING , 1999
"... Done well, separation of concerns can provide many software engineering benefits, including reduced complexity, improved reusability, and simpler evolution. The choice of boundaries for separate concerns depends on both requirements on the system and on the kind(s) of decompositionand composition a ..."
Abstract - Cited by 514 (8 self) - Add to MetaCart
given formalism supports. The predominant methodologies and formalisms available, however, support only orthogonal separations of concerns, along single dimensions of composition and decomposition. These characteristics lead to a number of well-known and difficult problems. This paper describes a new

Blind Signal Separation: Statistical Principles

by Jean-Francois Cardoso , 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
Abstract - Cited by 522 (4 self) - Add to MetaCart
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption

A Limited Memory Algorithm for Bound Constrained Optimization

by Richard H. Byrd, Richard H. Byrd, Peihuang Lu, Peihuang Lu, Jorge Nocedal, Jorge Nocedal, Ciyou Zhu, Ciyou Zhu - SIAM Journal on Scientific Computing , 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. ..."
Abstract - Cited by 557 (9 self) - Add to MetaCart
An algorithm for solving large nonlinear optimization problems with simple bounds is described.

The Elements of Statistical Learning -- Data Mining, Inference, and Prediction

by Trevor Hastie, Robert Tibshirani, Jerome Friedman
"... ..."
Abstract - Cited by 1320 (13 self) - Add to MetaCart
Abstract not found

Large margin methods for structured and interdependent output variables

by Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun - JOURNAL OF MACHINE LEARNING RESEARCH , 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract - Cited by 612 (12 self) - Add to MetaCart
that solves the optimization problem in polynomial time for a large class of problems. The proposed method has important applications in areas such as computational biology, natural language processing, information retrieval/extraction, and optical character recognition. Experiments from various domains
Results 1 - 10 of 1,313,515