Skip to content
Nonlinear optimization for MATLAB.
Branch: master
Clone or download
Latest commit 2bc59e2 May 4, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs Remove version number from documentation. Apr 26, 2019
tests tests: Adding tests for all poblano methods and some tests for TPLs (… May 4, 2019
.gitignore Adding .gitignore Apr 25, 2019
CONTRIBUTION_GUIDE.md Update CONTRIBUTION_GUIDE.md May 4, 2019
CONTRIBUTORS.md Update CONTRIBUTORS.md May 4, 2019
COPYRIGHT.txt Adding v1.1 with documentation refactoring Apr 25, 2019
Contents.m Updating version number to 1.2 Apr 26, 2019
INSTALL.md Update INSTALL.md Apr 26, 2019
LICENSE.txt Adding v1.1 with documentation refactoring Apr 25, 2019
README.md Adding v1.1 with documentation refactoring Apr 25, 2019
cstep.m Adding v1.1 with documentation refactoring Apr 25, 2019
cvsrch.m Adding v1.1 with documentation refactoring Apr 25, 2019
example1.m
example2.m Adding v1.1 with documentation refactoring Apr 25, 2019
example2_extract.m Adding v1.1 with documentation refactoring Apr 25, 2019
example2_init.m Adding v1.1 with documentation refactoring Apr 25, 2019
gradientcheck.m Adding v1.1 with documentation refactoring Apr 25, 2019
hessvec_fd.m Adding v1.1 with documentation refactoring Apr 25, 2019
info.xml Updating version number to 1.2 Apr 26, 2019
install_poblano.m Adding v1.1 with documentation refactoring Apr 25, 2019
lbfgs.m
ncg.m Adding v1.1 with documentation refactoring Apr 25, 2019
poblano_linesearch.m Adding v1.1 with documentation refactoring Apr 25, 2019
poblano_out.m poblano_out: updating checks for finite numbers (used to be checked f… May 4, 2019
poblano_params.m
tn.m tn: removing dead code. Fixes #3. May 4, 2019

README.md

Poblano Toolbox for MATLAB

Copyright 2009 National Technology & Engineering Solutions of Sandia,
LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
U.S. Government retains certain rights in this software.

Poblano is a Matlab toolbox of large-scale algorithms for unconstrained nonlinear optimization problems. The algorithms in Poblano require only first-order derivative information (e.g., gradients for scalar-valued objective functions), and therefore can scale to very large problems. The driving application for Poblano development has been tensor decompositions in data analysis applications (bibliometric analysis, social network analysis, chemometrics, etc.).

Poblano optimizers find local minimizers of scalar-valued objective functions taking vector inputs. The gradient (i.e., first derivative) of the objective function is required for all Poblano optimizers. The optimizers converge to a stationary point where the gradient is approximately zero. A line search satisfying the strong Wolfe conditions is used to guarantee global convergence of the Poblano optimizers. The optimization methods in Poblano include several nonlinear conjugate gradient methods (Fletcher-Reeves, Polak-Ribiere, Hestenes-Stiefel), a limited-memory quasi-Newton method using BFGS updates to approximate second-order derivative information, and a truncated Newton method using finite differences to approximate second-order derivative information.

You can’t perform that action at this time.