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Numerical Optimization
Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization inExpand
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On the limited memory BFGS method for large scale optimization
TLDR
We study the numerical performance of a limited memory quasi-Newton method for large scale optimization, which we call the L-BFGS method. Expand
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Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
TLDR
Optimization is an important tool used in decision science and for the analysis of physical systems . Expand
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Optimization Methods for Large-Scale Machine Learning
TLDR
This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Expand
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Updating Quasi-Newton Matrices With Limited Storage
We study how to use the BFGS quasi-Newton matrices to precondition minimization methods for problems where the storage is critical. We give an update formula which generates matrices usingExpand
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Numerical Optimization
Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization inExpand
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A Limited Memory Algorithm for Bound Constrained Optimization
TLDR
An algorithm for solving large nonlinear optimization problems with simple bounds is described. Expand
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A limited-memory algorithm for bound-constrained optimization
An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based on the gradient projection method and uses a limited-memory BFGS matrix to approximate theExpand
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Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
TLDR
L-BFGS-B is a limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. Expand
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Global Convergence Properties of Conjugate Gradient Methods for Optimization
TLDR
This paper explores the convergence of nonlinear conjugate gradient methods without restarts, and with practical line searches. Expand
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