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Under the terms of Contract DE-NA0003525 with NTESS, the\nU.S. Government retains certain rights in this software.\n```\n\nPoblano is a Matlab toolbox of large-scale algorithms for\nunconstrained nonlinear optimization problems. The algorithms in\nPoblano require only first-order derivative information (e.g.,\ngradients for scalar-valued objective functions), and therefore can\nscale to very large problems. The driving application for Poblano\ndevelopment has been tensor decompositions in data analysis\napplications (bibliometric analysis, social network analysis,\nchemometrics, etc.).\n\nPoblano optimizers find local minimizers of scalar-valued objective\nfunctions taking vector inputs. The gradient (i.e., first derivative)\nof the objective function is required for all Poblano optimizers. The\noptimizers converge to a stationary point where the gradient is\napproximately zero. A line search satisfying the strong Wolfe\nconditions is used to guarantee global convergence of the Poblano\noptimizers. The optimization methods in Poblano include several\nnonlinear conjugate gradient methods (Fletcher-Reeves, Polak-Ribiere,\nHestenes-Stiefel), a limited-memory quasi-Newton method using BFGS\nupdates to approximate second-order derivative information, and a\ntruncated Newton method using finite differences to approximate\nsecond-order derivative information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fpoblano_toolbox","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandialabs%2Fpoblano_toolbox","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandialabs%2Fpoblano_toolbox/lists"}