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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.

She received her M. Louis U. The main focus of her scientific activity has been on models and algorithms for complex combinatorial problems arising in different application contexts.

In particular, she works on linear and mixed integer linear programming models and her interests include exact methods, heuristics, metaheuristics and hybrid algorithms for vehicle routing and arc routing problems, knapsack problems, optimization problems in procurement and in finance. He received both his M. His research interests are focused on theoretical research, computer solutions and interdisciplinary applications in the area of linear and discrete optimization and decision support with the main stress on multiple criteria optimization and decision making under risk.

In particular, he works on problems of equitable location, fair resource allocation, portfolio optimization, preference modeling.

Her main scientific interests include branch-and-cut and branch-and-price algorithms for mixed integer linear programming problems, heuristics and metaheuristics , combinatorial optimization, worst-case and competitive analysis, applications of mixed integer linear models to finance and to transportation and logistics. She has been invited speaker and member of the scientific committee of several international conferences.

Linear and Mixed Integer Programming for Portfolio Optimization

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