Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

Numerical Optimization

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a h...

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Confirmation Bias: A Ubiquitous Phenomenon in Many Guises:

phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.

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Towards automated crystallographic structure refinement with phenix.refine

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Uncertainty, its modelling and analysis have been discussed across many literatures including statistics and operational research, knowledge management and philosophy: (i) adherents to Bayesian approaches have usually argued that uncertainty should either be modelled by probabilities or resolved by discussion that clarifies meaning; (ii) some have followed Knight in distinguishing between contexts of risk and of uncertainty: the former admitting modelling and analysis through probability; the latter not; (iii) there are also host of approaches in the literatures stemming from Zadeh’s concept of a fuzzy set; (iv) theories of sense-making in the philosophy and management literatures see knowledge and uncertainty as opposite extremes of human understanding and discuss the resolution of uncertainty accordingly. Here I provide a personal perspective, taking a Bayesian stance. However, I adopt a softer position than conventional and recognise the concerns in other approaches. In particular, I use the Cynefin framework of decision contexts to reflect on processes of modelling and analysis in statistical, risk and decision analysis. The approach builds on several recent strands of discussion that argue for a convergence of qualitative scenario planning ideas and more quantitative approaches to analysis. I discuss how these suggestions and discussions relate to some earlier thinking on the methodology of modelling and, in particular, the concept of a ‘small world’ articulated by Savage.

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Cynefin : uncertainty, small worlds and scenarios

Impact of compressor failures on gas transmission network capability

There are three contexts in which one might wish to combine expert judgments of uncertainty: the expert problem, the group decision problem, and the textbook problem. Much has been written on the first two, which have the focus of a single decision context, but little on the third. The textbook problem arises when one needs to draw together expert judgments into a decision analysis when their judgments were made originally in a context-free manner or perhaps for other decision contexts. In many ways the textbook problem parallels that of performing a meta-analysis of empirical studies. However, there are differences. In this paper, we discuss those difficulties and then focus on two closely related issues: How should expert judgment studies be published so as to facilitate subsequent meta-analyses, and how should such meta-analyses be performed?

Expert Judgment, Meta-analysis, and Participatory Risk Analysis

Radiation accidents such as those at Three Mile Island, Chernobyl and, more recently, Fukushima have emphasized the need for supporting all phases of emergency management from the early phases in which a threat is detected to years and decades after the accident. Several decision-aiding tools have been developed to prevent and mitigate the effects of a radiation accident. This work reviews a range of mathematical models, computing tools and, particularly, multi-criteria decision-making techniques that have been applied in the last 25 years to help politicians, health officials, local authority representatives and emergency planning officers devise better countermeasure strategies in the event of a radiation accident. The paper discusses all phases of a nuclear emergency as well as emergency training and planning. It highlights what has been achieved in the application of multi-criteria decision analysis, mainly through initiatives such as the EU-funded real-time online decision support projects. It examines how such tools that have been developed fit into the formulation, evaluation and appraisal stages of the emergency management process and discusses the complex socio-technical issues that arise from radiation accidents.

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Simon French

Papers

Cynefin : uncertainty, small worlds and scenarios

Impact of compressor failures on gas transmission network capability

Expert Judgment, Meta-analysis, and Participatory Risk Analysis

25 years of MCDA in nuclear emergency management

Decision, Probability and Utility: Selected Readings