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    Multicriteria Portfolio Construction with Python
    Modeling and Optimization in Space Engineering: State of the Art and New Challenges
    Ebook series2 titles

    Springer Optimization and Its Applications Series

    By Panos M. Pardalos, Elissaios Sarmas, Sergey I. Nikolenko and

    ()

    Currently unavailable

    Currently unavailable

    About this series

    This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field.  

    In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs.

    The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.

    LanguageEnglish
    PublisherSpringer
    Release dateDec 4, 2018
    Multicriteria Portfolio Construction with Python
    Modeling and Optimization in Space Engineering: State of the Art and New Challenges

    Titles in the series (2)

    • 144

      Modeling and Optimization in Space Engineering: State of the Art and New Challenges
      Modeling and Optimization in Space Engineering: State of the Art and New Challenges

      Modeling and Optimization in Space Engineering: State of the Art and New Challenges

      Giorgio Fasano
      0 ratings
      0 ratings

      This book presents advanced case studies that address a range of important issues arising in space engineering. An overview of challenging operational scenarios is presented, with an in-depth exposition of related mathematical modeling, algorithmic and numerical solution aspects. The model development and optimization approaches discussed in the book can be extended also towards other application areas. The topics discussed illustrate current research trends and challenges in space engineering as summarized by the following list:   •       Next Generation Gravity Missions •       Continuous-Thrust Trajectories by Evolutionary Neurocontrol •       Nonparametric Importance Sampling for Launcher Stage Fallout •       Dynamic System Control Dispatch •       OptimalLaunch Date of Interplanetary Missions •       Optimal Topological Design •       Evidence-Based Robust Optimization •       Interplanetary Trajectory Design by Machine Learning •       Real-Time Optimal Control •       Optimal Finite Thrust Orbital Transfers •       Planning and Scheduling of Multiple Satellite Missions •       Trajectory Performance Analysis •       Ascent Trajectory and Guidance Optimization •        Small Satellite Attitude Determination and Control •       Optimized Packings in Space Engineering •       Time-Optimal Transfers of All-Electric GEO Satellites   Researchers working on space engineering applications will find this work a valuable, practical source of information. Academics, graduate and post-graduate students working in aerospace, engineering, applied mathematics, operations research, and optimal control will find useful information regarding model development and solution techniques, in conjunction with real-world applications.              

      This book presents advanced case studies that address a range of important issues arising in space engineering. An overview of challenging operational scenarios is presented, with an in-depth exposition of related mathematical modeling, algorithmic and numerical solution aspects. The model development and optimization approaches discussed in the book can be extended also towards other application areas. The topics discussed illustrate current research trends and challenges in space engineering as summarized by the following list:   •       Next Generation Gravity Missions •       Continuous-Thrust Trajectories by Evolutionary Neurocontrol •       Nonparametric Importance Sampling for Launcher Stage Fallout •       Dynamic System Control Dispatch •       OptimalLaunch Date of Interplanetary Missions •       Optimal Topological Design •       Evidence-Based Robust Optimization •       Interplanetary Trajectory Design by Machine Learning •       Real-Time Optimal Control •       Optimal Finite Thrust Orbital Transfers •       Planning and Scheduling of Multiple Satellite Missions •       Trajectory Performance Analysis •       Ascent Trajectory and Guidance Optimization •        Small Satellite Attitude Determination and Control •       Optimized Packings in Space Engineering •       Time-Optimal Transfers of All-Electric GEO Satellites   Researchers working on space engineering applications will find this work a valuable, practical source of information. Academics, graduate and post-graduate students working in aerospace, engineering, applied mathematics, operations research, and optimal control will find useful information regarding model development and solution techniques, in conjunction with real-world applications.              

    • 163

      Multicriteria Portfolio Construction with Python
      Multicriteria Portfolio Construction with Python

      Multicriteria Portfolio Construction with Python

      Elissaios Sarmas
      0 ratings
      0 ratings

      This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub. This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters.  A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered.  In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions. The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.  

      This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub. This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters.  A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered.  In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions. The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.  

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