Algorithm Portfolios
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Algorithm Portfolios
Author: Dimitris Souravlias
language: en
Publisher: Springer Nature
Release Date: 2021-03-24
This book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, and open problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.
Automatic Algorithm Selection for Complex Simulation Problems
Author: Roland Ewald
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-11-20
To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments without demanding expert knowledge on simulation. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and describes its integration into the open-source modelling and simulation framework James II. Its selection mechanisms are able to cope with three situations: no prior knowledge is available, the impact of problem features on simulator performance is unknown, and a relationship between problem features and algorithm performance can be established empirically. The author concludes with an experimental evaluation of the developed methods.
Algorithms for Solving Financial Portfolio Design Problems: Emerging Research and Opportunities
In the current scope of economics, the management of client portfolios has become a considerable problem within financial institutions due to the amount of risk that goes into assigning assets. Various algorithmic models exist for solving these portfolio challenges; however, considerable research is lacking that further explains these design problems and provides applicable solutions to these imperative issues. Algorithms for Solving Financial Portfolio Design Problems: Emerging Research and Opportunities is a pivotal reference source that provides vital research on the application of various programming models within the financial engineering field. While highlighting topics such as landscape analysis, breaking symmetries, and linear programming, this publication analyzes the quadratic constraints of current portfolios and provides algorithmic solutions to maximizing the full value of these financial sets. This book is ideally designed for financial strategists, engineers, programmers, mathematicians, banking professionals, researchers, academicians, and students seeking current research on recent mathematical advances within financial engineering.