Here is a book devoted to well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and semidefinite programming. The authors present the basic theory underlying these problems as well as their numerous applications in engineering, including synthesis of filters, Lyapunov stability analysis, and structural design. The authors also discuss the complexity issues and provide an overview of the basic theory of state-of-the-art polynomial time interior point methods for linear, conic quadratic, and semidefinite programming. The book's focus on well-structured convex problems in conic form allows for unified theoretical and algorithmical treatment of a wide spectrum of important optimization problems arising in applications.
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Book Description
Here is a book devoted to well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and semidefinite programming. The authors present the basic theory underlying these problems as well as their numerous applications in engineering, including synthesis of filters, Lyapunov stability analysis, and structural design.
About the Author
Aharon Ben-Tal is a professor at the Technion-Israel Institute of Technology and head of the MINERVA Optimization Center. He serves on the editorial boards of several journals, including Mathematics of Operations Research, SIAM Journal on Optimization, Journal of Convex Analysis, and Mathematical Modeling and Numerical Algorithms. Arkadi Nemirovski also is a Professor at the Technion-Israel Institute of Technology. The author of three monographs, Dr. Nemirovski is the joint recipient of the Fulkerson Prize of the Mathematical Programming Society and AMS (1982) and the Dantzig Prize of the Mathematical Programming Society and SIAM (1991).
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4 of 5 people found the following review helpful:
4.0 out of 5 stars
A good reference source for convex optimization,
This review is from: Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications (MPS-SIAM Series on Optimization) (Paperback)
Lectures on Convex Optimization is devoted to well structuredand efficiently solvable convex optimization problems, with an emphasis on conic quadratic and semidefinite programming. The authors begin with linear programming, and then progress to conic programming. [I really enjoyed their description of the transition from linear to general conic programming!]. They then discuss two special conic optimization problems namely second order cone programming, and semidefinite programming. Numerous applications of conic programming espcially in filter design, Lyapunov stability analysis, and structural design are presented. The book concludes with a discussion on the computational tractability of convex programs, and primal dual interior point algorithms to solving general conic optimization problems. One can then take on the likes of Renegar's recent book My only minor comments are :- All in all, a good workout and an encyclopedic source for
0 of 1 people found the following review helpful:
4.0 out of 5 stars
A good reference source for convex optimization,
This review is from: Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications (MPS-SIAM Series on Optimization) (Paperback)
Lectures on Convex Optimization is devoted to well structuredand efficiently solvable convex optimization problems, with an emphasis on conic quadratic and semidefinite programming. The authors begin with linear programming, and then progress to conic programming. [I really enjoyed their description of the transition from linear to general conic programming!]. They then discuss two special conic optimization problems namely second order cone programming, and semidefinite programming. Numerous applications of conic programming espcially in filter design, Lyapunov stability analysis, and structural design are presented. The book concludes with a discussion on the computational tractability of convex programs, and primal dual interior point algorithms to solving general conic optimization problems. One can then take on the likes of Renegar's recent book My only minor comments are :- All in all, a good workout and an encyclopedic source for
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