mathematical programming

Wednesday, August 23, 2017 - 9:00am - 9:45am
Jean-Paul Watson (Sandia National Laboratories)
Pyomo ( is a mature and widely used Python library for expressing and solving a wide range of mathematical programs, i.e., algebraic optimization models. Pyomo allows users to specify optimization models with linear, non-linear (including differential algebraic and ordinary differential equations), mixed discrete-continuous, and stochastic components. These models can then be solved with a wide range of commercial and open source solvers, with varying capabilities.
Friday, September 14, 2012 - 2:20pm - 2:35pm
Anil Misra (University of Kansas)
Cohesive granular materials are known for their pressure-dependent strain softening behavior that is intimately linked to their microstructure and grain interactions. To understand the relationship of mechanical properties, composition and structure of these materials, we have been developing continuum theories utilizing higher displacement gradients and micromechanics [1-5]. In this presentation, we will first describe the second gradient continuum theory using the principal of virtual work to establish the governing equations and the boundary conditions.
Thursday, October 21, 2010 - 11:00am - 12:00pm
Michael Ferris (University of Wisconsin, Madison)
Co-authors: Steven Dirkse, Jan Jagla, Alexander Meeraus.

Traditional modeling approaches for mathematical programs have limitations.
Tuesday, November 18, 2008 - 2:45pm - 3:30pm
Leo Liberti (École Polytechnique)
If a mathematical program (be it linear or nonlinear) has many symmetric optima, solving it via Branch-and-Bound techniques often yields search trees of disproportionate sizes; thus, finding and exploiting symmetries is an important task. We propose a method for: (a) automatically finding the formulation group of any given Mixed-Integer Nonlinear Program, and (b) reformulating the problem so that it has fewer symmetric solutions.
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