On handling uncertainties in optimization problem, one way is to consider worst-case optimization.
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An alternative way is stochastic programming, which is quite useful in portfolio optimization and finance risk management. Most recent summary on stochastic programming. Absil , R. Mahony and R. This book is a good summary of optimizations on manifolds which is veryful in signal processing, data mining and statistical analysis.
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Chu, B. This is a good article on ADMM algorithms. Tulino and S. Greg W. Anderson , A. Guionnet and O. Louis L. Steven M. I - Estimation Theory , Prentice Hall, Harry L. Van Trees , Kristine L.
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Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers. Here, Kay helps readers develop strong intuition and expertise in designing well-performing algorithms that solve real-world problems. Kay begins by reviewing methodologies for developing signal processing algorithms, including mathematical modeling, computer simulation, and performance evaluation. He links concepts to practice by presenting useful analytical results and implementations for design, evaluation, and testing.
Exercises are presented throughout, with full solutions, and executable MATLAB code that implements all the algorithms, is provided on the accompanying CD.
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