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Algorithm, MatLab (Statistical/mathematical software) -- Usage, Algorithms -- Analysis, and Algorithms -- Usage

Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to psychometrics. We describe four MATLAB classes for tensor manipulations that can be used for fast algorithm prototyping. The tensor class extends the functionality of MATLAB's multidimensional arrays by supporting additional operations such as tensor multiplication. The tensor_as_matrix class supports the 'matricization' of a tensor, that is, the conversion of a tensor to a matrix (and vice versa), a commonly used operation in many algorithms. Two additional classes represent tensors stored in decomposed formats: cp_tensor and tucker_tensor. We describe all of these classes and then demonstrate their use by showing how to implement several tensor algorithms that have appeared in the literature. Categories and Subject Descriptors: G.4 [Mathematics of Computing]: Mathematical Software--Algorithm design and analysis; G.1.m [Mathematics of Computing]: Numerical Analysis--Miscellaneous General Terms: Algorithms Additional Key Words and Phrases: Higher-order tensors, multilinear algebra, N-way arrays, MATLAB

Computer programming, Algorithm, and Algorithms

PROTOMOL is a high-performance framework in C++ for rapid prototyping of novel algorithms for molecular dynamics and related applications. Its flexibility is achieved primarily through the use of inheritance and design patterns (object-oriented programming). Performance is obtained by using templates that enable generation of efficient code for sections critical to performance (generic programming). The framework encapsulates important optimizations that can be used by developers, such as parallelism in the force computation. Its design is based on domain analysis of numerical integrators for molecular dynamics (MD) and of fast solvers for the force computation, particularly due to electrostatic interactions. Several new and efficient algorithms are implemented in PROTOMOL. Finally, it is shown that PROTOMOL'S sequential performance is excellent when compared to a leading MD program, and that it scales well for moderate number of processors. Binaries and source codes for Windows, Linux, Solaris, IRIX, HP-UX, and AIX platforms are available under open source license at http://protomol, sourceforge.net. Categories and Subject Descriptors: D.1.5 [Programming Techniques]: Object-oriented programming; D.2.11 [Software Engineering]: Software Architectures--Domain specific architectures; D.2.13 [Software Engineering]: Reusable Software--Reusable libraries; G.1.7 [Numerical Analysis]: Ordinary Differential Equations--Multistep and multivalue methods; G.4 [Mathematical Software]:Algorithm design and analysis, efficiency, parallel and vector implementations, user interfaces; J.2 [Physical Sciences and Engineering]: Chemistry, Physics; J.3 [Life and Medical Sciences]: Biology and Genetics General Terms: Algorithms, Design, Performance Additional Key Words and Phrases: Fast electrostatic methods, incremental parallelism, molecular dynamics, multigrid, multiple time-stepping integration, object-oriented framework.

Mathematical software -- Usage, FORTRAN -- Usage, Mathematical programming -- Research, and Algorithms -- Research

A system consisting of an interval data type and a symbolic form of automatic repeating differentiation is being developed for research algorithms, using Fortran 90, to solve numerical nonlinear equation and global optimization codes. This system also provides extensive data structures to remove the programming burden.

Computer network equipment industry -- Product development, Programming languages -- Analysis, Prototypes, Engineering -- Analysis, Computer science -- Research, and Concurrent engineering -- Analysis

1. INTRODUCTION During the last decades, particular attention has been focused on concurrent programming within the computer science community. A concurrent program specifies two or more processes that cooperate in [...]
Concurrent programming is conceptually harder to undertake and to understand than sequential programming, because a programmer has to manage the coexistence and coordination of multiple concurrent activities. To alleviate this task several high-level approaches to concurrent programming have been developed. For some high-level programming approaches, prototyping for facilitating early evaluation of new ideas is a central goal. Prototyping is used to explore the essential features of a proposed system through practical experimentation before its actual implementation to make the correct design choices early in the process of software development. Approaches to prototyping concurrent applications with very high-level programming systems intend to alleviate the development in different ways. Early experimentation with alternate design choices or problem decompositions for concurrent applications is suggested to make concurrent programming easier. This paper presents a survey of programming languages and systems for prototyping concurrent applications to review the state of the art in this area. The surveyed approaches are classified with respect to the prototyping process. Categories and Subject Descriptors: D.1.3 [Programming Techniques]: Concurrent Programming -- Parallel programming; Distributed programming; D.2.1 [Software Engineering]: Requirements/Specifications; D.2.2 [Software Engineering]: Design Tools and Techniques -- Computer-aided software engineering (CASE); Petri nets; Software libraries; D.2.6 [Software Engineering]: Programming Environments -- Interactive environments; D.3.2 [Programming Languages]: Language Classifications -- Concurrent, distributed, and parallel languages; Very high-level languages; D.3.3 [Programming Languages]: Language Constructs and Features -- Concurrent programming structures General Terms: Languages Additional Key Words and Phrases: Concurrency, distribution, parallelism, rapid prototyping, very high-level languages

Software Engineering, Prototype, Large-Scale Systems, Data Structures, Graphs, and Reliability

Interest in applying prototyping to the development of large-scale, production software is increasing. This brings with it a corresponding increase in efforts to develop suitable languages and tools for prototyping. Rapid development and easy modification are the two fundamental requirements for prototyping software systems. Techniques for defining data objects can be specification described, implementation described, or value described. Three implementations of object definition techniques are IRIS, a graph-based scheme to represent the semantics of software-system descriptions; GRAPHITE, a system to generate packages to manipulate directed graphs; and PIC, a language framework and analysis technique to precisely describe and analyze module interfaces.

Differential equations -- Analysis, Java (Computer program language) -- Analysis, and Problem solving -- Analysis

Problem-solving environments (PSEs) offer a powerful yet flexible and convenient means for general experimentation with computational methods, algorithm prototyping, and visualization and manipulation of data. Consequently, PSEs have become the modus operandi of many computational scientists and engineers. However, despite these positive aspects, PSEs typically do not offer the level of granularity required by the specialist or algorithm designer to conveniently modify the details. In other words, the level at which PSEs are black boxes is often still too high for someone interested in modifying an algorithm as opposed to trying an alternative. In this article, we describe odeToJava, a Java-based PSE for initial-value problems in ordinary differential equations. odeToJava implements explicit and linearly implicit implicit-explicit Runge-Kutta methods with error and stepsize control and intra-step interpolation (dense output), giving the user control and flexibility over the implementational aspects of these methods. We illustrate the usage and functionality of odeToJava by means of computational case studies of initial-value problems (IVPs). Categories and Subject Descriptors: D.2.11 [Software Engineering]: Software Architectures--Domain-specific architectures; Patterns; G.1.7 [Numerical Analysis]: Ordinary Differential Equations--Initial value problems; One-step (single step) methods; G.4 [Mathematical Software]: Algorithm design and analysis; User interfaces General Terms: Algorithms, Design, Experimentation Additional Key Words and Phrases: Adaptive integration, additive Runge-Kutta, geometric integration, object-oriented, problem solving environment, Runge-Kutta, software architecture, stepsize-control, Stormer-Verlet DOI: http://dx.doi.org/10.1145/2641563

Statistical/mathematical software, Algorithm, Mathematical software -- Usage, Calculus -- Research, Algebraic topology -- Research, Topology -- Research, and Algorithms -- Usage

This article describes the algorithms, features, and implementation of PyDEC, a Python library for computations related to the discretization of exterior calculus. PyDEC facilitates inquiry into both physical problems on manifolds as well as purely topological problems on abstract complexes. We describe efficient algorithms for constructing the operators and objects that arise in discrete exterior calculus, lowest-order finite element exterior calculus, and in related topological problems. Our algorithms are formulated in terms of high-level matrix operations which extend to arbitrary dimension. As a result, our implementations map well to the facilities of numerical libraries such as NumPy and SciPy. The availability of such libraries makes Python suitable for prototyping numerical methods. We demonstrate how PyDEC is used to solve physical and topological problems through several concise examples. Categories and Subject Descriptors: G.4 [Mathematical Software]: ; G.1.8 [Numerical Analysis]: Partial Differential Equations--Finite element methods, finite volume methods; I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling--Geometric algorithms, languages, and systems General Terms: Algorithms, Theory, Experimentation, Design Additional Key Words and Phrases: Discrete exterior calculus, finite element exterior calculus, Whitney form, simplicial complex, cubical complex, Vietoris-Rips complex, computational topology, boundary operator, coboundary operator, chain, cochain DOI = 10.1145/2382585.2382588 http://doi.acm.org/10.1145/2382585.2382588

Programming Language, PROLOG, Compiler/decompiler, and Algorithm

The use of Prolog as a language offers advantages for describing succinctly most of the algorithms needed in prototyping and implementing compilers, or producing tools that facilitate the task of compiling. One approach in implementing compilers using Prolog consists of coupling actions to recursive descent parsers to produce syntax-trees, which are utilized in guiding the generation of assembly code. Prolog is not only used in parsing and compiling, but is a labor-saving device in prototyping and implementing many non-numerical algorithms which arise in compiling. Unification and nondeterminism as means to circumvent costly unnecessary features are also discussed. Other topics include: bottom-up and top-down parsers; syntax-directed translation; grammar properties; code generation; and newly proposed features for compiler construction.

Algorithm, Algorithms -- Methods, Algorithms -- Usage, Matrices -- Methods, and Matrices -- Usage

A standard Fortran 95 module for printing scalars, vectors, and matrices to external files is provided. The module can display variables of default kind of all intrinsic types (integer, real, complex, logical, and character), and add-on modules are provided for data of the nondefault kind. The main module is self-contained and incorporating it only requires that it be compiled and linked with a program containing a 'use dispmodule' statement. A generic interface and optional parameters are used, so that the same subroutine name, DISP, is used to display items of different data type and rank, irrespective of display options. The subroutine is quite versatile, and hopefully can improve Fortran's competitiveness against other array programming languages. The module also contains a function TOSTRING to convert numerical scalars and vectors to strings. Categories and Subject Descriptors: D.2.2 [Software Engineering]: Design Tools and Techniques--Software libraries; D.2.5 [Software Engineering]: Testing and Debugging--Debugging aids; D.4.4 [Operating Systems]: Communications Management--Input/output; D.3 [Programming Languages]; E.1 [Data Structures]--Arrays; G.4 [Mathematical Software]--User interfaces; H.5.2 [Information Interfaces and Presentation]: User Interfaces--Prototyping, screen design (e.g., text, graphics, color), user-centered design General Terms: Algorithms, Design Additional Key Words and Phrases: Fortran 95, matrix pretty-printing, matrix printing, output utilities, array programming language ACM Reference Format: Jonasson, K. 2009. Algorithm 892: DISPMODULE, a Fortran 95 module for pretty-printing matrices. ACM Trans. Math. Softw. 36, 1, Article 6 (March 2009), 7 pages. DOI = 10.1145/1486525.1486531 http://doi.acm.org/10.1145/1486525.1486531