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.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

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.

Finite Algorithms in Optimization and Data Analysis

David Snowden's Cynefin framework, introduced to articulate discussions of sense-making, knowledge management and organisational learning, has much to offer discussion of statistical inference and decision analysis. I explore its value, particularly in its ability to help recognise which analytic and modelling methodologies are most likely to offer appropriate support in a given context. The framework also offers a further perspective on the relationship between scenario thinking and decision analysis in supporting decision makers.

https://link.springer.com/content/pdf/10.1057%2Fjors.2012.23.pdf

Cynefin, Statistics and Decision Analysis

We explore ways for expressing and communicating uncertainties about food risks and the options for including views from different stakeholders, including members of the public, in risk assessment and risk management Uncertainty is not only a major consideration of technical risk assessments but also needs to be understood within the two-way interaction with various stakeholders, ranging from specialist risk managers to members of the public We consider techniques for integration of technical risk assessment methodologies, including measures of the uncertainties, with social science inputs on participation processes, consumer behaviour and effective risk communication We use case studies representing different types of risk – chemical contamination, and microbial contamination – to focus the discussion The paper reviews the approach of ongoing interdisciplinary research which is exploring ways of communicating risks and involving members of the public and other stakeholders in defining the interfaces and processes needed

Managing Food Chain Risks: Integrating Technical and Stakeholder Perspectives on Uncertainty

A role for human reliability analysis (HRA) in preventing drinking water incidents and securing safe drinking water.

In this paper, we discuss the functionality and interfaces of five MCDM software packages. We recognize that no single package is appropriate for all decision contexts and processes. Thus our emphasis is not so much to compare the functionality of the packages per se, but to consider their fit with different decision making processes. In doing so, we hope to provide potential users with guidance on selecting a package that is more compatible with their needs. Moreover, we reflect on the further functionality which we would believe should be developed and included in MCDM packages. Copyright � 2006 John Wiley & Sons, Ltd.

Simon French

Papers

Finite Algorithms in Optimization and Data Analysis

Cynefin, Statistics and Decision Analysis

Managing Food Chain Risks: Integrating Technical and Stakeholder Perspectives on Uncertainty

A role for human reliability analysis (HRA) in preventing drinking water incidents and securing safe drinking water.

Comparison study of multi-attribute decision analytic software