Analytics for Cyber Network Defense

This report provides a brief survey of analytics tools considered relevant to cyber network defense (CND). Ideas and tools come from fields such as statistics, data mining, and knowledge discovery. Some analytics are considered standard mathematical or statistical techniques, while others reflect current research directions. In all cases the report attempts to explain the relevance to CND with brief examples

Integrated Modeling, Mapping, and Simulation (IMMS) Framework for Exercise and Response Planning

EmergenCy management personnel at federal, stale, and local levels can benefit from the increased situational awareness and operational efficiency afforded by simulation and modeling for emergency preparedness, including planning, training and exercises. To support this goal, the Department of Homeland Security's Science & Technology Directorate is funding the Integrated Modeling, Mapping, and Simulation (IMMS) program to create an integrating framework that brings together diverse models for use by the emergency response community. SUMMIT, one piece of the IMMS program, is the initial software framework that connects users such as emergency planners and exercise developers with modeling resources, bridging the gap in expertise and technical skills between these two communities. SUMMIT was recently deployed to support exercise planning for National Level Exercise 2010. Threat, casualty. infrastructure, and medical surge models were combined within SUMMIT to estimate health care resource requirements for the exercise ground truth

HOPSPACK 2.0 user manual.

HOPSPACK (Hybrid Optimization Parallel Search PACKage) solves derivative-free optimization problems using an open source, C++ software framework. The framework enables parallel operation using MPI or multithreading, and allows multiple solvers to run simultaneously and interact to find solution points. HOPSPACK comes with an asynchronous pattern search solver that handles general optimization problems with linear and nonlinear constraints, and continuous and integer-valued variables. This user manual explains how to install and use HOPSPACK to solve problems, and how to create custom solvers within the framework

Fast Energy Minimization of large Polymers Using Constrained Optimization

A new computational technique is described that uses distance constraints to calculate empirical potential energy minima of partially rigid molecules. A constrained minimuzation algorithm that works entirely in Cartesian coordinates is used. The algorithm does not obey the constraints until convergence, a feature that reduces ill-conditioning and allows constrained local minima to be computed more quickly than unconstrained minima. Computational speedup exceeds the 3-fold factor commonly obtained in constained molecular dynamics simulations, where the constraints must be strictly obeyed at all times

Parallel Computation Chemistry Using Constraints: Final Report, LDRD 97-0301, Case 3504140000

Computer modeling to estimate material properties, design chem/bio sensors, and evaluate protein-protein interactions all require solving force field equations for molecular structures that contain tens of thousands of covalently connected atoms. Potential energy minimization is a key step in the calculation, but stiff covalent bonding forces make optimization difficult and expensive. This two-year LDRD developed two classes of advanced minimization algorithms that were specialized for chemistry applications and distributed computing machines. The project led to two successful algorithms that were implemented in three Sandia computational chemistry codes to support various users

HOPSPACK 2.0 user manual.

HOPSPACK (Hybrid Optimization Parallel Search PACKage) solves derivative-free optimization problems using an open source, C++ software framework. The framework enables parallel operation using MPI or multithreading, and allows multiple solvers to run simultaneously and interact to find solution points. HOPSPACK comes with an asynchronous pattern search solver that handles general optimization problems with linear and nonlinear constraints, and continuous and integer-valued variables. This user manual explains how to install and use HOPSPACK to solve problems, and how to create custom solvers within the framework

On the implementation of an algorithm for large-scale equality constrained optimization

Abstract. This paper describes a software implementation of Byrd and Omojokun’s trust region algorithm for solving nonlinear equality constrained optimization problems. The code is designed for the efficient solution of large problems and provides the user with a variety of linear algebra techniques for solving the subproblems occurring in the algorithm. Second derivative information can be used, but when it is not available, limited memory quasi-Newton approximations are made. The performance of the code is studied using a set of difficult test problems from the CUTE collection

C%2B%2B tensor toolbox user manual.

The C++ Tensor Toolbox is a software package for computing tensor decompositions. It is based on the Matlab Tensor Toolbox, and is particularly optimized for sparse data sets. This user manual briefly overviews tensor decomposition mathematics, software capabilities, and installation of the package. Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors in C++. The Toolbox compiles into libraries and is intended for use with custom applications written by users

EFFICIENTLY COMPUTING TENSOR EIGENVALUES ON A GPU

Abstract. The tensor eigenproblem has many important applications, and both mathematical and applicationspecific communities have taken recent interest in the properties of tensor eigenpairs as well as methods for computing them. In particular, Kolda and Mayo [3] present a generalization of the matrix power method for symmetric tensors. We focus in this work on efficient implementation of their algorithm, known as the shifted symmetric higher-order power method, and on how a GPU can be used to accelerate the computation up to 70 × over a sequential implementation for an application involving many small tensor eigenproblems