
This document provides a user manual for the SGOPT software library. SGOPT is a C++ class library for nonlinear optimization. This library uses an objectoriented 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 highlevel description of the optimization algorithms provided by SGOPT andmore »

SGOPT is a C++ library that includes implementations of several algorithms for stochastic global optimization and derivative free optimization.

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 »

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.

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 performanceguaranteed 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 »

This report describes the design of PICO, a C++ framework for implementing general parallel branchandbound algorithms. The PICO framework provides a mechanism for the efficient implementation of a wide range of branchandbound methods on an equally wide range of parallel computing platforms. We first discuss the basic architecture of PICO, including the application class hierarchy and the package's serial and parallel layers. We next describe the design of the serial layer, and its central notion of manipulating subproblem states. Then, we discuss the design of the parallel layer, which includes flexible processor clustering and communication rates, various load balancing mechanisms,more »

The authors describe a naturalistic behavioral model for the simulation of small unit combat. This model, Klein's recognitionprimed decision making (RPD) model, is driven by situational awareness rather than a rational process of selecting from a set of action options. They argue that simulated combatants modeled with RPD will have more flexible and realistic responses to a broad range of smallscale combat scenarios. Furthermore, they note that the predictability of a simulation using an RPD framework can be easily controlled to provide multiple evaluations of a given combat scenario. Finally, they discuss computational issues for building an RPDbased behavior enginemore »
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