Physical Sciences and Mathematics Commons^™

Forcing Optimality And Brandt's Principle, Domenico Napoletani, Marco Panza, Daniele C. Struppa

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

We argue that many optimization methods can be viewed as representatives of “forcing”, a methodological approach that attempts to bridge the gap between data and mathematics on the basis of an a priori trust in the power of a mathematical technique, even when detailed, credible models of a phenomenon are lacking or do not justify the use of this technique. In particular, we show that forcing is implied in particle swarms optimization methods, and in modeling image processing problems through optimization. From these considerations, we extrapolate a principle for general data analysis methods, what we call ‘Brandt’s principle’, namely the …

Preface To "Intertwingled: The Work And Influence Of Ted Nelson", Douglas R. Dechow, Daniele C. Struppa

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

This is the preface to "Intertwingled: The Work and Influence of Ted Nelson", which examines and honors the work and influence of the computer visionary and re-imagines its meaning for the future. Emerging from a conference held in 2014 at Chapman University, it includes contributions from world-renowned computer scientists and media figures.

The full text of this book is available on an open access basis at Springer.

The blog for the Intertwingled Conference can be read here.

Using Mapreduce Streaming For Distributed Life Simulation On The Cloud, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable …

Distributed Simulated Annealing With Mapreduce, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

Simulated annealing’s high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used for simulated annealing yet. In this paper, we investigate the applicability of MapReduce to distributed simulated annealing in general, and to the TSP in particular. We (i) design six algorithmic patterns of distributed simulated annealing with MapReduce, (ii) instantiate the patterns into MR implementations to solve a sample TSP problem, and …

Shared Memory, Message Passing, And Hybrid Merge Sorts For Standalone And Clustered Smps, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

While merge sort is well-understood in parallel algorithms theory, relatively little is known of how to implement parallel merge sort with mainstream parallel programming platforms, such as OpenMP and MPI, and run it on mainstream SMP-based systems, such as multi-core computers and multi-core clusters. This is misfortunate because merge sort is not only a fast and stable sort algorithm, but it is also an easy to understand and popular representative of the rich class of divide-and-conquer methods; hence better understanding of merge sort parallelization can contribute to better understanding of divide-and-conquer parallelization in general. In this paper, we investigate three …

Digital Cs1 Study Pack Based On Moodle And Python, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

We believe that CS1 courses can be made more attractive to students:

• by teaching a highly interactive scripting language – Python
• by using an open source course management system - such as Moodle - to make all course resources available in a comprehensive digital study pack, and
• by offering detailed self-guided online labs

We have used Moodle [1] and Python [2] to develop a "Python First" digital study pack [3] which comprises a wealth of new, original learning modules: extensive e-texts, detailed self-guided labs, numerous sample programs, quizzes, and slides. Our digital study pack pedagogy is described in recent ITiCSE …

Derivation Of Secure Parallel Applications By Means Of Module Embedding, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

An enhancement to modular languages called module embedding facilitates the development and utilization of secure generic parallel algorithms.

Development And Utilization Of Parallel Generic Algorithms For Scientific Computations, Atanas Radenski, Andrew Vann, Boyana Norris

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

We develop generic parallel algorithms as extensible modules that encapsulate related classes and parallel methods. Extensible modules define common parallel structures, such as meshes, pipelines, or master-server networks in problem-independent manner. Such modules can be extended with sequential domain-specific code in order to derive particular parallel applications. In this paper, we first outline the essence of extensible modules. Then, we focus on a case study of the cellular automaton, a message-parallel generic algorithm from which we derive diverse parallel scientific applications.

Parallel Probabilistic Computations On A Cluster Of Workstations, Atanas Radenski, Andrew Vann, Boyana Norris

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

Probabilistic algorithms are computationally intensive approximate methods for solving intractable problems. Probabilistic algorithms are excellent candidates for cluster computations because they require little communication and synchronization. It is possible to specify a common parallel control structure as a generic algorithm for probabilistic cluster computations. Such a generic parallel algorithm can be glued together with domain-specific sequential algorithms in order to derive approximate parallel solutions for different intractable problems.

In this paper we propose a generic algorithm for probabilistic computations on a cluster of workstations. We use this generic algorithm to derive specific parallel algorithms for two discrete optimization problems: the …

Is Oberon As Simple As Possible?, Atanas Radenski

Mathematics, Physics, and Computer Science Faculty Books and Book Chapters

The design of the programming language Oberon was led by the quote by Albert Einstein: 'make it as simple as possible, but not simpler'. The objective of this paper is to analyze some design solutions and propose alternatives which could both simplify and strengthen the language without making it simpler than possible. The paper introduces one general concept, the module type, which can be used to represent records, modules, and eventually procedures. Type extension is redefined in terms of component nesting and incomplete designators. As a result, type extension supports multiple inheritance.