Multicriteria Decision Analysis Methods
The discussion of the portfolio management problem in the previous chapter signified the need for new methodological decision support frameworks, in order to overcome the existing problems and cure the inadequacies of the conventional mean–variance model.
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Elissaios Sarmas Panos Xidonas Haris Doukas
Multicriteria Portfolio Construction with Python
Springer Optimization and Its Applications Volume 163
Series Editors Panos M. Pardalos , University of Florida My T. Thai , University of Florida Honorary Editor Ding-Zhu Du, University of Texas at Dallas Advisory Editors Roman V. Belavkin, Middlesex University John R. Birge, University of Chicago Sergiy Butenko, Texas A&M University Vipin Kumar, University of Minnesota Anna Nagurney, University of Massachusetts Amherst Jun Pei, Hefei University of Technology Oleg Prokopyev, University of Pittsburgh Steffen Rebennack, Karlsruhe Institute of Technology Mauricio Resende, Amazon Tamás Terlaky, Lehigh University Van Vu, Yale University Guoliang Xue, Arizona State University Yinyu Ye, Stanford University
Aims and Scope Optimization has continued to expand in all directions at an astonishing rate. New algorithmic and theoretical techniques are continually developing and the diffusion into other disciplines is proceeding at a rapid pace, with a spot light on machine learning, artificial intelligence, and quantum computing. Our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in areas not limited to applied mathematics, engineering, medicine, economics, computer science, operations research, and other sciences. The series Springer Optimization and Its Applications (SOIA) aims to publish state-of-the-art expository works (monographs, contributed volumes, textbooks, handbooks) that focus on theory, methods, and applications of optimization. Topics covered include, but are not limited to, nonlinear optimization, combinatorial optimization, continuous optimization, stochastic optimization, Bayesian optimization, optimal control, discrete optimization, multi-objective optimization, and more. New to the series portfolio include Works at the intersection of optimization and machine learning, artificial intelligence, and quantum computing. Volumes from this series are indexed by Web of Science, zbMATH, Mathematical Reviews, and SCOPUS.
More information about this series at http://www.springer.com/series/7393
Elissaios Sarmas • Panos Xidonas • Haris Doukas
Multicriteria Portfolio Construction with Python
Elissaios Sarmas Electrical and Computer Engineering National Technical University of Athens Athens, Greece
Panos Xidonas ESSCA École de Management Paris, France
Haris Doukas Electrical and Computer Engineering National Technical University of Athens Athens, Greece
ISSN 1931-6828 ISSN 1931-6836 (electronic) Springer Optimization and Its Applications ISBN 978-3-030-53742-5 ISBN 978-3-030-53743-2 (eBook) https://doi.org/10.1007/978-3-030-53743-2 Mathematics Subject Classification: 90-XX, 90-10, 90Bxx, 90Cxx © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This wor
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