БИБЛИОТЕКА MIPCL ДЛЯ РЕШЕНИЯ ЗАДАЧ СМЕШАННО-ЦЕЛОЧИСЛЕННОГО ПРОГРАММИРОВАНИЯ

The Mixed Integer Programming Class Library (MIPCL) is a free software designed for implementing mixed integer programming models quickly, easily, and efficiently. Computational experiments show that currently MIPCL is one of the best noncommercial mixed-integer programming solvers. MIPCL libraries, documentation and examples are provided under the terms of the GNU Lesser Public License. Therefore, MIPCL is equally freely available for noncommercial and commercial use.Рассматривается библиотека MIPCL – свободное программное обеспечение, предназначенное для быстрой и эффективной компьютерной реализации моделей смешанно целочисленного программирования (СЦП). Вычислительные эксперименты доказали, что сегодня MIPCL является наиболее эффективным свободным инструментом для решения задач СЦП. Библиотека MIPCL, документация и многочисленные примеры предоставляются на условиях лицензии GLPL (GNU Lesser Public License), поэтому MIPCL свободно доступна как для некоммерческого, так и коммерческого использования

Faces of submodular functions and related topics

Available from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, Duesternbrook Weg 120, D-24105 Kiel / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

MIPCL LIBRARY FOR SOLVING MIXED INTEGER PROGRAMMING PROBLEMS

The Mixed Integer Programming Class Library (MIPCL) is a free software designed for implementing mixed integer programming models quickly, easily, and efficiently. Computational experiments show that currently MIPCL is one of the best noncommercial mixed-integer programming solvers. MIPCL libraries, documentation and examples are provided under the terms of the GNU Lesser Public License. Therefore, MIPCL is equally freely available for noncommercial and commercial use

Module: Mathematical Economics

Полный текст документа доступен пользователям сети БГУ

Module: Mathematical Economics

Полный текст документа доступен пользователям сети БГУ

Games through Nested Fixpoints

In this paper we consider two-player zero-sum payoff games on finite graphs, both in the deterministic as well as in the stochastic setting. In the deterministic setting, we consider total-payoff games which have been introduced as a refinement of mean-payoff games [18, 10]. In the stochastic setting, our class is a turn-based variant of liminf-payoff games [15, 16, 4]. In both settings, we provide a non-trivial characterization of the values through nested fixpoint equations. The characterization of the values of liminf-payoff games moreover shows that solving liminf-payoff games is polynomial-time reducible to solving stochastic parity games. We construct practical algorithms for solving the occurring nested fixpoint equations based on strategy iteration. As a corollary we obtain that solving deterministic total-payoff games and solving stochastic liminf-payoff games is in UP ∩ co−UP

Faster Algorithms for Mean-payoff Games

In this paper, we study algorithmic problems for quantitative models that are motivated by the applications in modeling embedded systems. We consider two-player games played on a weighted graph with mean-payoff objective and with energy constraints. We present a new pseudopolynomial algorithm for solving such games, improving the best known worst-case complexity for pseudopolynomial mean-payoff algorithms. Our algorithm can also be combined with the procedure by Andersson and Vorobyov to obtain a randomized algorithm with currently the best expected time complexity. The proposed solution relies on a simple fixpoint iteration to solve the log-space equivalent problem of deciding the winner of energy games. Our results imply also that energy games and mean-payoff games can be reduced to safety games in pseudopolynomial time. © 2010 Springer Science+Business Media, LLC.SCOPUS: ar.jinfo:eu-repo/semantics/publishe