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Provides information on the inverse problem, design and optimization symposium to be held in Rio de Janeiro, Brazil from March 17 to 19, 2004. Motivation and objectives; Symposium topics; Symposium fees.

The application of quantum-behaved particle swarm optimization to multiobjective problems has attracted more and more attention recently. However, in order to extend quantum-behaved particle swarm optimization to multiobjective context, two major problems, namely the selection of personal and global best positions and the maintenance of population diversity, need to be taken into consideration. In this paper, a novel Cultural MOQPSO algorithm is proposed, in which cultural evolution mechanism is introduced into quantum-behaved particle swarm optimization to deal with multiobjective problems. In Cultural MOQPSO, the exemplar positions of each particle are obtained according to “belief space,” which contains different types of knowledge. Moreover, to increase population diversity and obtain continuous and even-distributed Pareto fronts, a combination-based update operator is proposed to update the external population in this paper. A comprehensive comparison of Cultural MOQPSO with some state-of-the-art evolutionary algorithms on several benchmark test functions, including ZDT, DTLZ and CEC2009 test instances, demonstrates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified framework for opportunistic fair scheduling in wireless systems. We consider a TDMA type of multiple access scheme, in which only one user can be scheduled in each time-slot. For opportunistic fair scheduling in such a system, some nice frameworks have been developed in the previous works, such as Agrawal and Subramanian (Allerton conference on communication, control and computing, 2002), Liu et al. (IEEE Journal of Selected Areas in Communications 19(10): 2053–2065, 2001) and Liu et al. (Computer Networks 41(4): 451–474, 2003). However, in this paper, we consider a more general problem that can accommodate more general types of fairness, and more general types of utility functions than those in the previous works. In addition to those generalizations, we develop a new framework for opportunistic fair scheduling based on the duality theory, which is different from those in the previous works. The duality theory is a well-defined theory in the mathematical optimization area. Hence, it can provide a unified framework for many different types of problems. In fact, we show that two different frameworks in Agrawal and Subramanian (Allerton conference on communication, control and computing, 2002), Liu et al. (IEEE Journal of Selected Areas in Communications 19(10): 2053–2065, 2001) and Liu et al. (Computer Networks 41(4): 451–474, 2003) are special cases of ours. In addition, by using the unified framework developed in this paper, we can not only develop various opportunistic fair scheduling schemes but also analyze the developed algorithm more rigorously and systematically. [ABSTRACT FROM AUTHOR]

We are presenting in this special issue selected, peer-reviewed, papers that were presented at the 1st International Symposium and 10th Balkan Conference on Operational Research (BALCOR 2011), which was held during September 22-24, 2011, in Thessaloniki, Greece. [ABSTRACT FROM PUBLISHER]

The article presents information on the 10th International Conference on Operations Research held March 6-9, 2012 in Havana, Cuba. Lectures delivered at the conference are noted. Lists are presented of lectures, discussions and research papers presented at the conference on topics including operations research, statistics and mathematical optimization.

Abstract: In this paper we study optimization problems with verifiable one-parameter selfish agents introduced by Auletta et al. [V. Auletta, R. De Prisco, P. Penna, P. Persiano, The power of verification for one-parameter agents, in: Proceedings of the 31st International Colloquium on Automata, Languages and Programming, ICALP, in: LNCS, vol. 3142, 2004, pp. 171–182]. Our goal is to allocate load among the agents, provided that the secret data of each agent is a single positive real number: the cost they incur per unit load. In such a setting the payment is given after the load completion, therefore if a positive load is assigned to an agent, we are able to verify if the agent declared to be faster than she actually is. We design truthful mechanisms when the agents’ type sets are upper-bounded by a finite value. We provide a truthful mechanism that is -approximate if the underlying algorithm is -approximate and weakly-monotone. Moreover, if type sets are also discrete, we provide a truthful mechanism preserving the approximation ratio of its algorithmic part. Our results improve the existing ones which provide truthful mechanisms dealing only with finite type sets and do not preserve the approximation ratio of the underlying algorithm. Finally, we give applications for our payment schemes. Firstly, we give a full characterization of the problem by using our techniques. Even if our payment schemes need upper-bounded type sets, every instance of can be “mapped” into an instance with upper-bounded type sets preserving the approximation ratio. In conclusion, we turn our attention to binary demand games. In particular, we show that the Minimum Radius Spanning Tree admits an exact truthful mechanism with verification achieving time (and space) complexity of the fastest centralized algorithm for it. This contrasts with a recent truthful mechanism for the same problem [G. Proietti, P. Widmayer, A truthful mechanism for the non-utilitarian minimum radius spanning tree problem, in: Proceedings of the 17th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA, ACM Press, 2005, pp. 195–202] which pays a linear factor with respect to the complexity of the fastest centralized algorithm. Such a result is extended to several binary demand games studied in literature. [Copyright &y& Elsevier]

Owing to a technical problem in the submission process, selected presentations from the 7th International Conference on Boundary Elements, which took place in Paris, France, in September 2006 (http://beteq.engineeringconferences.net/), will not be published in a separate special issue. Links to these papers as they were published in the journal are listed at the end of this editorial. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various papers presented during the International Symposium on Mathematical Programming (ISMP) in Pittsburgh, Pennsylvania from July 12-17, 2015 including coordinate descent algorithms, copositive optimization and mathematical optimization.

In recent years, the research in the field of optimization has been very active in the Asia-Pacific region, particularly in China and Japan. To promote research activities further and bring significant impact on the development of research, the Second Japanese-Sino Optimization Meeting (JSOM2002) was held in Kyoto, Japan, on September 25-27, 2002. About 130 participants not only from Asia but also from North America and Europe attended the meeting and exchanged their ideas on the latest work in optimization and related areas. In response to our call-for-papers to a special issue for the meeting, a significant number of manuscripts were submitted for possible publication. After careful review, 17 papers have finally been accepted. Reflecting the wide scope of the meeting, these papers cover extremely diverse topics of theory and applications of methods in continuous optimization, discrete optimization, dynamic optimization, and stochastic optimization. They are published in this and the next issues of Optimization Methods & Software. We are thankful to all authors who submitted their papers to the special issue and all anonymous referees who kindly helped us by reviewing the manuscripts carefully. Last but not least, we are grateful to Oleg Burdakov for his constant and generous support. [ABSTRACT FROM AUTHOR]

Information about several papers discusses in 11th International Conference on Stochastic Programming which was held in Vienna, Austria on August 27, 2007 is presented. The conference was aimed at closing the gap between the field of stochastic programming and the area of robust optimization and optimization under uncertainty. The conference featured Arkadi Nemirovski, Darinka Dentcheva and Andrzej Ruszczynski.

Information about several papers discussed at the International Symposium on Physical Design is presented. It features new methods for extraction of spatial correlation and discusses placement-related problems such as optimization of wire lengths, 3-dimensional floorplanning, and the impact of clustering methods on placement algorithms. Classic topics including routing, decoupling capacitor optimization, and buffer insertion were also included.

The article introduces the current issue, which contains selected papers from the "Scientific Meeting 'Numerical Methods for Local and Global Optimization: Sequential and Parallel Algorithms,' including one about a local optima smoothing approach for solving global optimization problems, and another suggesting a method for constructing test functions for global optimization.