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.
tensor decomposition, tensor factorization, CANDECOMP, PARAFAC, optimization
@techreport{SAND2010-1422,
author = {Daniel M. Dunlavy and Tamara G. Kolda and Evrim Acar},
title = {Poblano v1.0: A Matlab Toolbox for Gradient-Based Optimization},
number = {SAND2010-1422},
institution = {Sandia National Laboratories},
month = {March},
year = {2010},
doi = {10.2172/989350},
url = {http://www.osti.gov/scitech/biblio/989350},
urldate = {2014-04-17},
}