J.F. Bonnans is an expert in convex analysis and dynamic optimization, both in the deterministic and stochastic setting. His main contributions deal with the sensitivity analysis of optimization problems, high order optimality conditions, optimal control and stochastic control. He worked on quantization methods for stochastic programming problems, on the approximate dynamic programming for problems with monotone value function, and on sparse linear regression.

“The book is mainly devoted to the theoretical study of concepts of stochastic programming. … The book offers a solid theoretical background for researchers/students/practitioners keen on disposing of a rigorous foundation of stochastic programming.” (Wim van Ackooij, Mathematical Reviews, November, 2019)

• 1 A convex optimization toolbox.- 2 Semidefinite and semiinfinite programming.- 3 An integration toolbox.- 4 Risk measures.- 5 Sampling and optimizing.- 6 Dynamic stochastic optimization.- 7 Markov decision processes.- 8 Algorithms.- 9 Generalized convexity and transportation theory.- References.- Index.

J.F. Bonnans is an expert in convex analysis and dynamic optimization, both in the deterministic and stochastic setting. His main contributions deal with the sensitivity analysis of optimization problems, high order optimality conditions, optimal control and stochastic control. He worked on quantization methods for stochastic programming problems, on the approximate dynamic programming for problems with monotone value function, and on sparse linear regression.

“The book is mainly devoted to the theoretical study of concepts of stochastic programming. … The book offers a solid theoretical background for researchers/students/practitioners keen on disposing of a rigorous foundation of stochastic programming.” (Wim van Ackooij, Mathematical Reviews, November, 2019)

• 1 A convex optimization toolbox.- 2 Semidefinite and semiinfinite programming.- 3 An integration toolbox.- 4 Risk measures.- 5 Sampling and optimizing.- 6 Dynamic stochastic optimization.- 7 Markov decision processes.- 8 Algorithms.- 9 Generalized convexity and transportation theory.- References.- Index.

J.F. Bonnans is an expert in convex analysis and dynamic optimization, both in the deterministic and stochastic setting. His main contributions deal with the sensitivity analysis of optimization problems, high order optimality conditions, optimal control and stochastic control. He worked on quantization methods for stochastic programming problems, on the approximate dynamic programming for problems with monotone value function, and on sparse linear regression.

“The book is mainly devoted to the theoretical study of concepts of stochastic programming. … The book offers a solid theoretical background for researchers/students/practitioners keen on disposing of a rigorous foundation of stochastic programming.” (Wim van Ackooij, Mathematical Reviews, November, 2019)

• 1 A convex optimization toolbox.- 2 Semidefinite and semiinfinite programming.- 3 An integration toolbox.- 4 Risk measures.- 5 Sampling and optimizing.- 6 Dynamic stochastic optimization.- 7 Markov decision processes.- 8 Algorithms.- 9 Generalized convexity and transportation theory.- References.- Index.