Jean B. Lasserre

Global Optimization with Polynomials and the Problem of Moments

We consider the problem of finding the unconstrained global minimum of a real-valued polynomial p(x): {\mathbb{R}}^n\to {\mathbb{R}}

Discrete-time Markov control processes : basic optimality criteria

, as well as the global minimum of p(x), in a compact set K defined by polynomial inequalities. It is shown that this problem reduces to solving an (often finite) sequence of convex linear matrix inequality (LMI) problems. A notion of Karush--Kuhn--Tucker polynomials is introduced in a global optimality condition. Some illustrative examples are provided.

Moments, positive polynomials and their applications

1 Introduction and Summary.- 1.1 Introduction.- 1.2 Markov control processes.- 1.3 Preliminary examples.- 1.4 Summary of the following chapters.- 2 Markov Control Processes.- 2.1 Introduction.- 2.2 Markov control processes.- 2.3 Markov policies and the Markov property.- 3 Finite-Horizon Problems.- 3.1 Introduction.- 3.2 Dynamic programming.- 3.3 The measurable selection condition.- 3.4 Variants of the DP equation.- 3.5 LQ problems.- 3.6 A consumption-investment problem.- 3.7 An inventory-production system.- 4 Infinite-Horizon Discounted-Cost Problems.- 4.1 Introduction.- 4.2 The discounted-cost optimality equation.- 4.3 Complements to the DCOE.- 4.4 Policy iteration and other approximations.- 4.5 Further optimality criteria.- 4.6 Asymptotic discount optimality.- 4.7 The discounted LQ problem.- 4.8 Concluding remarks.- 5 Long-Run Average-Cost Problems.- 5.1 Introduction.- 5.2 Canonical triplets.- 5.3 The vanishing discount approach.- 5.4 The average-cost optimality inequality.- 5.5 The average-cost optimality equation.- 5.6 Value iteration.- 5.7 Other optimality results.- 5.8 Concluding remarks.- 6 The Linear Programming Formulation.- 6.1 Introduction.- 6.2 Infinite-dimensional linear programming.- 6.3 Discounted cost.- 6.4 Average cost: preliminaries.- 6.5 Average cost: solvability.- 6.6 Further remarks.- Appendix A Miscellaneous Results.- Appendix B Conditional Expectation.- Appendix C Stochastic Kernels.- Appendix D Multifunctions and Selectors.- Appendix E Convergence of Probability Measures.- References.

GloptiPoly: Global optimization over polynomials with Matlab and SeDuMi

. From a theoretical viewpoint, the GPM has developments and impact in var-ious area of Mathematics like algebra, Fourier analysis, functional analysis, operator theory, probabilityand statistics, to cite a few. In addition, and despite its rather simple and short formulation, the GPMhas a large number of important applications in various fields like optimization, probability, mathematicalfinance, optimal control, control and signal processing, chemistry, cristallography, tomography, quantumcomputing, etc.In its full generality, the GPM is untractable numerically. However when K is a compact basic semi-algebraic set, and the functions involved are polynomials (and in some cases piecewise polynomials orrational functions), then the situation is much nicer. Indeed, one can define a systematic numerical schemebased on a hierarchy of semidefinite programs, which provides a monotone sequence that converges tothe optimal value of the GPM. (A semidefinite program is a convex optimization problem which up toarbitrary fixed precision, can be solved in polynomial time.) Sometimes finite convergence may evenocccur.In the talk, we will present the semidefinite programming methodology to solve the GPM and describein detail several applications of the GPM (notably in optimization, probability, optimal control andmathematical finance).R´ef´erences[1] J.B. Lasserre, Moments, Positive Polynomials and their Applications, Imperial College Press, inpress.[2] J.B. Lasserre, A Semidefinite programming approach to the generalized problem of moments,Math. Prog. 112 (2008), pp. 65–92.

GloptiPoly 3: moments, optimization and semidefinite programming

GloptiPoly is a Matlab/SeDuMi add-on to build and solve convex linear matrix inequality relaxations of the (generally nonconvex) global optimization problem of minimizing a multivariable polynomial function subject to polynomial inequality, equality, or integer constraints. It generates a series of lower bounds monotonically converging to the global optimum without any problem splitting. Global optimality is detected and isolated optimal solutions are extracted automatically. Numerical experiments show that for most of the small-scale problems described in the literature, the global optimum is reached at low computational cost.

Convergent relaxations of polynomial matrix inequalities and static output feedback

We describe a major update of our Matlab freeware GloptiPoly for parsing generalized problems of moments and solving them numerically with semidefinite programming.

An Explicit Exact SDP Relaxation for Nonlinear 0-1 Programs

Using a moment interpretation of recent results on sum-of-squares decompositions of nonnegative polynomial matrices, we propose a hierarchy of convex linear matrix inequality (LMI) relaxations to solve nonconvex polynomial matrix inequality (PMI) optimization problems, including bilinear matrix inequality (BMI) problems. This hierarchy of LMI relaxations generates a monotone sequence of lower bounds that converges to the global optimum. Results from the theory of moments are used to detect whether the global optimum is reached at a given LMI relaxation, and if so, to extract global minimizers that satisfy the PMI. The approach is successfully applied to PMIs arising from static output feedback design problems.

Convergent SDP-Relaxations in Polynomial Optimization with Sparsity

We consider the general nonlinear optimization problem in 0- 1 variables and provide an explicit equivalent convex positive semidefinite program in 2n - 1 variables. The optimal values of both problems are identical. From every optimal solution of the former one easily find an optimal solution of the latter and conversely, from every solution of the latter one may construct an optimal solution of the former.

Markov Chains and Invariant Probabilities

We consider a polynomial programming problem

An Explicit Equivalent Positive Semidefinite Program for Nonlinear 0-1 Programs

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