Multi-objective Portfolio Optimization by Mixed Integer Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multi-objective Portfolio Optimization by Mixed Integer Programming write by Bartosz Sawik. This book was released on 2011. Multi-objective Portfolio Optimization by Mixed Integer Programming available in PDF, EPUB and Kindle.
Linear and Mixed Integer Programming for Portfolio Optimization
Linear and Mixed Integer Programming for Portfolio Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Linear and Mixed Integer Programming for Portfolio Optimization write by Renata Mansini. This book was released on 2015-06-10. Linear and Mixed Integer Programming for Portfolio Optimization available in PDF, EPUB and Kindle. This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
Multiobjective Linear and Integer Programming
Multiobjective Linear and Integer Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Multiobjective Linear and Integer Programming write by Carlos Henggeler Antunes. This book was released on 2016-04-08. Multiobjective Linear and Integer Programming available in PDF, EPUB and Kindle. This book opens the door to multiobjective optimization for students in fields such as engineering, management, economics and applied mathematics. It offers a comprehensive introduction to multiobjective optimization, with a primary emphasis on multiobjective linear programming and multiobjective integer/mixed integer programming. A didactic book, it is mainly intended for undergraduate and graduate students, but can also be useful for researchers and practitioners. Further, it is accompanied by an interactive software package - developed by the authors for Windows platforms - which can be used for teaching and decision-making support purposes in multiobjective linear programming problems. Thus, besides the textbook’s coverage of the essential concepts, theory and methods, complemented with illustrative examples and exercises, the computational tool enables students to experiment and enhance their technical skills, as well as to capture the essential characteristics of real-world problems.
A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization
A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization write by Stefan Rocktäschel. This book was released on 2020-01-21. A Branch-and-Bound Algorithm for Multiobjective Mixed-integer Convex Optimization available in PDF, EPUB and Kindle. Stefan Rocktäschel introduces a branch-and-bound algorithm that determines a cover of the efficient set of multiobjective mixed-integer convex optimization problems. He examines particular steps of this algorithm in detail and enhances the basic algorithm with additional modifications that ensure a more precise cover of the efficient set. Finally, he gives numerical results on some test instances.
Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming
Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming write by Perez C.. This book was released on 2017-08-16. Operations Research. Optimization With Matlab. Multiobjective, Quadratic and Mixed Programming available in PDF, EPUB and Kindle. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. Optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would desire a design that is both light and rigid. When two objectives conflict, a trade-off must be created. There may be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and rigidity. The set of trade-off designs that cannot be improved upon according to one criterion without hurting another criterion is known as the Pareto set. The curve created plotting weight against stiffness of the best designs is known as the Pareto frontier.A design is judged to be "Pareto optimal" (equivalently, "Pareto efficient" or in the Pareto set) if it is not dominated by any other design: If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal. The choice among "Pareto optimal" solutions to determine the "favorite solution" is delegated to the decision maker. In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other. In some cases, the missing information can be derived by interactive sessions with the decision maker.Multi-objective optimization problems have been generalized further into vector optimization problems where the (partial) ordering is no longer given by the Pareto ordering.Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer.Classical optimization techniques due to their iterative approach do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary algorithms, however, are a very popular approach to obtain multiple solutions in a multi-modal optimization task.This book develops the following topics:* "Multiobjective Optimization Algorithms" * "Using fminimax with a Simulink Model" * "Signal Processing Using fgoalattain" * "Generate and Plot a Pareto Front" * "Linear Programming Algorithms" * "Maximize Long-Term Investments Using Linear Programming" * "Mixed-Integer Linear Programming Algorithms" * "Tuning Integer Linear Programming" * "Mixed-Integer Linear Programming Basics" * "Optimal Dispatch of Power Generators" * "Mixed-Integer Quadratic Programming Portfolio Optimization" * "Quadratic Programming Algorithms"* "Quadratic Minimization with Bound Constraints" * "Quadratic Minimization with Dense, Structured Hessian"* "Large Sparse Quadratic Program with Interior Point Algorithm" * "Least-Squares (Model Fitting) Algorithms" * "lsqnonlin with a Simulink Model" * "Nonlinear Least Squares With and Without Jacobian" * "Linear Least Squares with Bound Constraints" * "Optimization App with the lsqlin Solver" * "Maximize Long-Term Investments Using Linear Programming" * "Jacobian Multiply Function with Linear Least Squares" * "Nonlinear Curve Fitting with lsqcurvefit" * "Fit a Model to Complex-Valued Data" * "Systems of Equations" * "Nonlinear Equations with Analytic Jacobian" * "Nonlinear Equations with Jacobian" * "Nonlinear Equations with Jacobian Sparsity Pattern"* "Nonlinear Systems with Constraints" * "Parallel Computing for Optimization"
