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...

/pdf/confirmation-bias-a-ubiquitous-phenomenon-in-many-guises-18m0fi8wck.pdf

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.

Reader's Guide. Foreword to the English-Language Edition. Introductory: How to Aid Whom with What Types of Decision. 1. Decision Problems and Processes. 2. Decision Aiding: Major Actors and the Role of Models. 3. Reference Examples. 4. Phases and Options of an Approach to Decision Aiding (General Ideas of the Methodology). Level I: How to Determine What is Possible and in What Terms to Formulate a Problem. 5. Actions and Decision Aiding. 6. Problematics as Guides in Decision Aiding. Level II: How to Determine Preferences and on What Bases. 7. Preference, Indifference, Incomparability: Binary Relations and Basic Structures. 8. Comparing Actions and Modeling Consequences. 9. Comparing Actions and Developing Criteria. Levels III and IV: How to Proceed from Multiple Criteria to Comprehensive Preferences and Develop Recommendations. 10. Coherent Criterion Family and Decision Aiding in the Description Problematic. 11. Modeling Comprehensive Preferences: Three Operational Approaches for Progressing Beyond the Description Problematic. 12. Specific Difficulties in Choice, Sorting, and Ranking Problematics. Bibliography. Index.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2600981.pdf

Multicriteria Methodology for Decision Aiding

A method is described which produces sensible estimates of structure factor moduli from intensity observations, whether the latter are positive or negative. Preliminary applications of the method to data from the protein phosphorylase b are summarized.

On the treatment of negative intensity observations

Decision Theory: An Introduction to the Mathematics of Rationality

Keywords: Decision-Making ; multiobjective analysis Reference Record created on 2005-06-20, modified on 2016-08-08

https://link.springer.com/content/pdf/10.1057%2Fjors.1983.105.pdf

Multi-Objective Decision Analysis with Engineering and Business Applications

This decision-centred approach has been widely hailed as a breakthrough in planning thought. It bridges the difficult gap between the aspirations of strategic planning and the realities of day-to-day management. The approach is specifically designed to help people plan under the familiar pressures of uncertainty, shortage of time, limited resources and conflicting interests. Since it first emerged from a pioneering study of British city government at work by operational researchers and social scientists, the approach has been applied to a wide range of organizational and interorganizational planning problems and has proved especially effective as an aid to interactive working in interdisciplinary groups throughout the world. This extensively illustrated book offers an authoritative, up-to-date guide to the subject. It is written not just as an introduction to the philosophy and techniques of strategic choice, but also as a handbook for quick reference by users. It includes illustrations from practical applications in several countries and will be invaluable not only to students of planning and academics, but also to practitioners working in any field of business management or public policy.

Simon French

Papers

Multicriteria Methodology for Decision Aiding

On the treatment of negative intensity observations

Decision Theory: An Introduction to the Mathematics of Rationality

Multi-Objective Decision Analysis with Engineering and Business Applications

Planning under pressure : the strategic choice approach