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  1. We describe new capabilities for modeling MPEC problems within the Pyomo modeling software. These capabilities include new modeling components that represent complementar- ity conditions, modeling transformations for re-expressing models with complementarity con- ditions in other forms, and meta-solvers that apply transformations and numeric optimization solvers to optimize MPEC problems. We illustrate the breadth of Pyomo's modeling capabil- ities for MPEC problems, and we describe how Pyomo's meta-solvers can perform local and global optimization of MPEC problems.
  2. This document provides a user manual for the SGOPT software library. SGOPT is a C++ class library for nonlinear optimization. This library uses an object-oriented design that allows the software to be extended to a new problem domains. Furthermore, this library was designed to that the interface is straightforward while providing flexibility to allow new algorithms to be easily added to this library. The SGOPT library has been used by several software projects at Sandia, and it is integrated into the DAKOTA design and analysis toolkit. This report provides a high-level description of the optimization algorithms provided by SGOPT andmore » describes the C++ class hierarchy in which they are implemented. Finally, installation instructions are included.« less
  3. SGOPT is a C++ library that includes implementations of several algorithms for stochastic global optimization and derivative free optimization.
  4. The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework ofmore » EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.« less
  5. For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.
  6. Crystal lattices are infinite periodic graphs that occur naturally in a variety of geometries and which are of fundamental importance in polymer science. Discrete models of protein folding use crystal lattices to define the space of protein conformations. Because various crystal lattices provide discretizations of the same physical phenomenon, it is reasonable to expect that there will exist invariants across lattices related to fundamental properties of the protein folding process. This paper considers whether performance-guaranteed approximability is such an invariant for HP lattice models. The authors define a master approximation algorithm that has provable performance guarantees provided that a specificmore » sublattice exists within a given lattice. They describe a broad class of crystal lattices that are approximable, which further suggests that approximability is a general property of HP lattice models.« less

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"Hart, William E."

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