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Stochastic and Global Optimization
In the paper we propose a model of tax incentives optimization for inve- ment projects with a help of the mechanism of accelerated depreciation. Unlike the tax holidays which influence on effective income tax rate, accelerated - preciation affects on taxable income. In modern economic practice the state actively use for an attraction of - vestment into the creation of new enterprises such mechanisms as accelerated depreciation and tax holidays. The problem under our consideration is the following. Assume that the state (region) is interested in realization of a certain investment project, for ex- ple, the creation of a new enterprise. In order to attract a potential investor the state decide...
Topics in Semidefinite and Interior-Point Methods
Contains papers presented at a workshop held at The Fields Institute in May 1996. Papers are arranged in sections on theory, applications, and algorithms. Specific topics include testing the feasibility of semidefinite programs, semidefinite programming and graph equipartition, the totally nonnegative completion problem, approximation clustering, and cutting plane algorithms for semidefinite relaxations. For graduate students and researchers in mathematics, computer science, engineering, and operations. No index. Annotation copyrighted by Book News, Inc., Portland, OR
From Local to Global Optimization
The book consists of research papers based on results presented at a conference held in Sweden to celebrate Hoang Tuy's achievements in Optimization. The collection is dedicated to Professor Tuy on the occasion of his 70th birthday. The papers appear in alphabetical order by first author and cover a wide range of recent results in Mathematical Programming. The work of Hoang Tuy, in particular in Global Optimization, has provided directions for new algorithmic developments in the field. Audience: Faculty, graduate students, and researchers in mathematical programming, computer science and engineering.
Encyclopedia of Optimization
The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".
Binary Neural Networks
Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including par...
Models and Algorithms for Global Optimization
The research of Antanas Zilinskas has focused on developing models for global optimization, implementing and investigating the corresponding algorithms, and applying those algorithms to practical problems. This volume, dedicated to Professor Zilinskas on the occasion of his 60th birthday, contains new survey papers in which leading researchers from the field present various models and algorithms for solving global optimization problems.
Randomization Methods in Algorithm Design
This volume is based on proceedings held during the DIMACS workshop on Randomization Methods in Algorithm Design in December 1997 at Princeton. The workshop was part of the DIMACS Special Year on Discrete Probability. It served as an interdisciplinary research workshop that brought together a mix of leading theorists, algorithmists and practitioners working in the theory and implementation aspects of algorithms involving randomization. Randomization has played an important role in the design of both sequential and parallel algorithms. The last decade has witnessed tremendous growth in the area of randomized algorithms. During this period, randomized algorithms went from being a tool in compu...
Mathematical Reviews
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The Holocaust and Soviet War Crimes Trials in the Cold War Context
This volume aims to offer a fresh perspective towards the evaluation of Soviet war crimes trials of Holocaust perpetrators, their representation through various means of media, and their reception in the context of the Cold War. By examining the 1964 Klaipėda war crimes trial in Soviet Lithuania through a microhistorical perspective, the book explores the history of the “second wave” of Soviet justice in the 1960s. It attempts to offer insight not only into how this Soviet war crimes trial was initiated and investigated, but also into how it was presented in the courtroom and channeled through the media for publicity. The book argues that the war crimes trials conducted by the Soviet Lithuanian judiciary can be on one hand perceived as an intrinsic element of Soviet ideological propaganda and, on the other, viewed as an alternative space for disclosing memories of the mass murder of Jews, offering an opposing perspective to the official Soviet politics of memory. Intended for both an academic audience and the general public, this volume unveils an intertwined compilation of Soviet legal history, politics of retribution, memory, and media during the Thaw period.
Bayesian Optimization
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
