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Title: Poblano v1.0 : a Matlab toolbox for gradient-based optimization.

Abstract

We present Poblano v1.0, a Matlab toolbox for solving gradient-based unconstrained optimization problems. Poblano implements three optimization methods (nonlinear conjugate gradients, limited-memory BFGS, and truncated Newton) that require only first order derivative information. In this paper, we describe the Poblano methods, provide numerous examples on how to use Poblano, and present results of Poblano used in solving problems from a standard test collection of unconstrained optimization problems.

Authors:
;  [1];  [1]
  1. (Sandia National Laboratories, Livermore, CA)
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
989350
Report Number(s):
SAND2010-1422
TRN: US201019%%606
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; OPTIMIZATION; IMAGE PROCESSING; COMPUTERS; COMPUTER CODES; PROGRAMMING; Image processing.; Image analysis.; Calculus of tensors.

Citation Formats

Dunlavy, Daniel M., Acar, Evrim, and Kolda, Tamara Gibson. Poblano v1.0 : a Matlab toolbox for gradient-based optimization.. United States: N. p., 2010. Web. doi:10.2172/989350.
Dunlavy, Daniel M., Acar, Evrim, & Kolda, Tamara Gibson. Poblano v1.0 : a Matlab toolbox for gradient-based optimization.. United States. doi:10.2172/989350.
Dunlavy, Daniel M., Acar, Evrim, and Kolda, Tamara Gibson. Mon . "Poblano v1.0 : a Matlab toolbox for gradient-based optimization.". United States. doi:10.2172/989350. https://www.osti.gov/servlets/purl/989350.
@article{osti_989350,
title = {Poblano v1.0 : a Matlab toolbox for gradient-based optimization.},
author = {Dunlavy, Daniel M. and Acar, Evrim and Kolda, Tamara Gibson},
abstractNote = {We present Poblano v1.0, a Matlab toolbox for solving gradient-based unconstrained optimization problems. Poblano implements three optimization methods (nonlinear conjugate gradients, limited-memory BFGS, and truncated Newton) that require only first order derivative information. In this paper, we describe the Poblano methods, provide numerous examples on how to use Poblano, and present results of Poblano used in solving problems from a standard test collection of unconstrained optimization problems.},
doi = {10.2172/989350},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Mar 01 00:00:00 EST 2010},
month = {Mon Mar 01 00:00:00 EST 2010}
}

Technical Report:

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