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.

/pdf/towards-automated-crystallographic-structure-refinement-with-43hriikvv9.pdf

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.

A Bayesian method for calibration and aggregation of expert judgement

Decision Behaviour, Analysis and Support: Decision support systems

An outline of the uncertainty handling and data assimilation framework within a Bayesian frame is given, illustrated by its use within RODOS. Particular emphasis is paid to the need for compatible methodologies and data structures to hold uncertainty assessments throughout all the modules of a decision support system. Progress at developing techniques and modules is described: (i) to use belief nets to predict the source term when an accidental release threatens; (ii) to estimate the source term using a ring of gamma monitors at the periphery of the plant; and (iii) to estimate the source term from both near and more distant measurements. The management of uncertainties in the food chain modelling is briefly indicated. Finally, the issue of moving the basis of prediction from modelling approaches to databases of environmental measurements is discussed.

Probabilistic Data Assimilation within RODOS

The early designs for crisis management decision support systems used data-based or model-based methodologies and architectures. We argue that the complexity of crisis management situations means that a greater emphasis on collaboration is needed. Moreover, modern interactive Web 2.0 technologies allow group decision support to be offered to geographically dispersed teams. Given that crisis management often requires teams to be drawn together from a number of organisations sited at different locations, we reflect upon the potential of these technologies to support the early stages of crisis management without the need to draw the team together at a common location. We also report on a small scale experiment using GroupSystems ThinkTank to manage an emerging food safety event. We conclude that such systems have potential and deserve more careful evaluation.

Simon French

Papers

A Bayesian method for calibration and aggregation of expert judgement

Decision Behaviour, Analysis and Support: Decision support systems

Probabilistic Data Assimilation within RODOS

Web-Based Group Decision Support for Crisis Management

From the Editors: Decision Analysis Focus and Trends