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AbstractWhen applied to a problem which has more than one local optimal solution, most nonlinear programming algorithms will terminate with the first local solution found. Several methods have been suggested for extending the search to find the global optimum of such a nonlinear program. In this report we present the results of some numerical experiments designed to compare the performance of various strategies for finding the global solution.

This article proposes and develops a simple model of peace defense for the calculation of the optimal parameters of peace-building activity on different governmental and nongovernmental stages. The hierarchical structure of the model permits the use of different approaches and greater flexibility than existing models allow. At some stages of the model, the optimum criterion is the minimization of the probability of war, or (in a wider range) the "war potential"; at other stages, the criteria are more specific (for example, the minimization of time that is needed to realize the planned disarmament. Several qualitative results are derived from the model, including the formal description of a process of possible replacement of nuclear deterrence as a tool of international stability by the independent peace movement.

This book results from a long-term research effort aimed at tackling complex non-standard packing issues which arise in space engineering. The main research objective is to optimize cargo loading and arrangement, in compliance with a set of stringent rules. Complicated geometrical aspects are also taken into account, in addition to balancing conditions based on attitude control specifications. Chapter 1 introduces the class of non-standard packing problems studied. Chapter 2 gives a detailed explanation of a general model for the orthogonal packing of tetris-like items in a convex domain. A number of additional conditions are looked at in depth, including the prefixed orientation of subsets of items, the presence of unusable holes, separation planes and structural elements, relative distance bounds as well as static and dynamic balancing requirements. The relative feasibility sub-problem which is a special case that does not have an optimization criterion is discussed in Chapter 3. This setting can be exploited by introducing an ad hoc objective function, aimed at facilitating the finding of integer-feasible solutions. The third chapter also discusses the issue of tightening the general MIP model by introducing valid inequalities. A MIP-based heuristic approach is developed in Chapter 4, where the basic concept of abstract configuration is presented. Chapter 5 is devoted to experimental results relevant to a real-world application framework. Chapter 6 adopts both extensions of the general MIP model and non-linear formulations to tackle two further non-standard packing issues. The final Chapter 7 presents conclusions and provides insights regarding prospective developments (including non-standard scheduling aspects). Practitioners and researchers interested in advanced optimization model development and solution in the context of logistics, transportation systems, complex structures, manufacturing and electronics will find this book useful. The book can also be used in graduate courses on nonlinear - including global and mixed integer - optimization, as a valuable collection of practically meaningful object packing applications.

AbstractConcavity Cuts play an important role in concave minimization. In Porembski, J Global Optim 15 (1999), 371–404 we extended the concept underlying concavity cuts which led to the development of decomposition cuts. In numerical experiments with pure cutting plane algorithms for concave minimization, decomposition cuts have been shown to be superior to concavity cuts. However, three points remained open. First, how to derive decomposition cuts in the degenerate case. Second, how to ensure dominance of decomposition cuts over concavity cuts. Third, how to ensure the finite convergence of a pure cutting plane algorithm solely by decomposition cuts. These points will be addressed in this paper. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2008

[Abstract] Starting from an original portfolio of life insurance policies, in this article we propose a methodology to select model points portfolios that reproduce the original one, preserving its market risk under a certain measure. In order to achieve this goal, we first define an appropriate risk functional that measures the market risk associated to the interest rates evolution. Although other alternative interest rate models could be considered, we have chosen the LIBOR (London Interbank Offered Rate) market model. Once we have selected the proper risk functional, the problem of finding the model points of the replicating portfolio is formulated as a problem of minimizing the distance between the original and the target model points portfolios, under the measure given by the proposed risk functional. In this way, a high-dimensional global optimization problem arises and a suitable hybrid global optimization algorithm is proposed for the efficient solution of this problem. Some examples illustrate the performance of a parallel multi-CPU implementation for the evaluation of the risk functional, as well as the efficiency of the hybrid Basin Hopping optimization algorithm to obtain the model points portfolio. ; This research has been partially funded by EU H2020 MSCA-ITN-EID-2014 (WAKEUPCALL Grant Agreement 643045), Spanish MINECO (Grant MTM2016-76497-R) and by Galician Government with the grant ED431C2018/033, both including FEDER financial support. A.F., J.G. and C.V. also acknowledge the support received from the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01 ; Xunta de Galicia; ED431C2018/03 ; Xunta de Galicia; ED431G 2019/01