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The method of moments in dynamic optimization

The method of moments in dynamic optimization. Mathematics Seminar. Institute of Mathematics. Charles University. Prague, May 2005. R. Meziat Departamento de Matemáticas Universidad de los Andes Colombia, 2005. Index. Conic programming.

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The method of moments in dynamic optimization

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  1. The method of moments in dynamic optimization Mathematics Seminar. Institute of Mathematics. Charles University. Prague, May 2005 R. Meziat Departamento de Matemáticas Universidad de los Andes Colombia, 2005

  2. Index • Conic programming. • Convex envelopes • Microstructures • Optimal control The method of moments in dynamic optimization, R. Meziat, 2005.

  3. Conic programming Matrix of m rows and n columns: P and V are closed convex cones in Rn Polar cone: System of homogenous inequalities: Basic results: Positive cone generated by the rows of the matrix A The method of moments in dynamic optimization, R. Meziat, 2005.

  4. Conic programming Farkas Lemma: Theorem of the alternative I: Ax=b has a solution in x 0 of Rn or excluding: has solution in The method of moments in dynamic optimization, R. Meziat, 2005.

  5. Theorem of the alternative II: Ax b has a solution in x 0 of Rn or excluding: Theorem of the alternative III: Ax b has a solution in x  Rn or excluding: Conic programming has solution in: has solution in: The method of moments in dynamic optimization, R. Meziat, 2005.

  6. Conic programming Corollary: Consequences of the Farkas Lemma: • Kuhn-Tucker conditions in programs under restrictions in form of inequality. • Duality in convex programming. The method of moments in dynamic optimization, R. Meziat, 2005.

  7. Conic programming Duality gap: Given a couple of feasible solutions (x,y) Primal program (P) The system of lineal inequalities: Dual program (D) Has a solution (y,x,t)  Rm x Rn x R1 that satisfies: The method of moments in dynamic optimization, R. Meziat, 2005.

  8. Conic programming One of the following affirmations is always truth: • There is a couple of optimal solutions for the primal (P) and dual (D) that satisfies: • One of the problems is not limited and it is not feasible. One couple of feasible solutions is a couple of optimal solution if the complementariness relations are fulfilled: The method of moments in dynamic optimization, R. Meziat, 2005.

  9. Equivalence between convex cones and relations of order in linear spaces with inner product. Relation of compatible linear order with the topology and the operations of the underlying linear space Conic programming Order properties: • Reflexivity • Antisymetric • Transitive • Homogenous • Additive • Continuity The method of moments in dynamic optimization, R. Meziat, 2005.

  10. Conic programming Relationship between cones and orders: Given a closed cone pointed V and a lineal space E, the relation a  b defined as a-b  V fulfills all the properties. Example of cones: Positives octants in Euclidean Spaces: Lorentz cones: Positive semidefinite matrix cones. E: Space of n x n symmetric matrix with the interior product of Frobenius. V=S+n: Cone of positive semidefinite symmetrical matrix The method of moments in dynamic optimization, R. Meziat, 2005.

  11. Conic programming Primal program (P) • (P) and (D) are dual conic programs. • The duality gap is always positive: c x - b y  0 for every couple of feasible solutions (x, y) • When one of the problems (P) or (D) is limited and feasible, then the other one has a solutions and the optimal solution is the same • A couple of feasible solution (x, y) is composed by optimal solutions: Dual program (D) The method of moments in dynamic optimization, R. Meziat, 2005.

  12. Conic programming Examples of conic programs: • Steiner Min-Max problem: • Weighted Steiner problem: General form of the duality in the conic programming: Vi if the family of convex cones. Primal (P) Dual (D) The method of moments in dynamic optimization, R. Meziat, 2005.

  13. Conic programming Global optimization in bidimensional polynomials: be positive semidefinite Semidefinite relaxation: A necessary condition is that the values: be moments is that the matrix: The method of moments in dynamic optimization, R. Meziat, 2005.

  14. Conic programming Primal program (P) We suppose that the non negative polynomial: Dual program (D) can be expressed as: The method of moments in dynamic optimization, R. Meziat, 2005.

  15. Conic programming Solution: We take the coefficient of the expression: The feasible solution for (D) give us a inferior cote for (P) and we have that the inferior cote is m* for the relaxation of the global problem: As the feasible solution for the problem (D) which value of the feasible function of (D) is the same with m* The method of moments in dynamic optimization, R. Meziat, 2005.

  16. Convex envelopes We find the convex envelope of one-dimensional coercive polynomials given in the general form: Primal problem: The convex envelope in the point t is: Dual problem: The method of moments in dynamic optimization, R. Meziat, 2005.

  17. Convex envelopes We use the truncated Hamburger moment problem and we transform the problem in a semidefinite problem: The optimal measure has two forms: We characterize the moments using the Hankel matrix. The method of moments in dynamic optimization, R. Meziat, 2005.

  18. Convex envelopes Example 1: For t=0 For t=0.5 For t=1 The method of moments in dynamic optimization, R. Meziat, 2005.

  19. Convex envelopes Example 2: For t= 0 For t= 2 The method of moments in dynamic optimization, R. Meziat, 2005.

  20. Convex envelopes Example 3: For t= -0.5 The method of moments in dynamic optimization, R. Meziat, 2005.

  21. Microstructures General problem: • u is the displacement of each point respect to the starting point. • u’ is the unitary deformation • f internal energy of deformation. • y potential of external forces. This method is used to determine the microstructure in unidimensional elastic bars, which deformation potential is non-convex: Schematic curve of a typical potential of deformation for a steel The method of moments in dynamic optimization, R. Meziat, 2005.

  22. Microstructures We make an analysis of general models where the non-convex dependence of  in u’ can be written with a polynomial expression: The method of moments in dynamic optimization, R. Meziat, 2005.

  23. The original problem has a minimizer only if it is a Direc Delta If the original problem does not have a minimizer, there is a region I where the parametrized measure is supported by two points. This solution determines the oscillation of the solutions of the problem. Microstructures The method of moments in dynamic optimization, R. Meziat, 2005.

  24. Microstructures The method of moments in dynamic optimization, R. Meziat, 2005.

  25. Microstructures • The new problem is an convex optimization problem in the variable m, thus the existence of the minimizer is guaranteed • The non-lineal problem is in the restriction imposed by the moment characterization. • The way that the problem has taken an ideal form in order to solve it with software for non-linear programming. • The solution of the relaxed problem tell us wheater the original problem has solution or not. The method of moments in dynamic optimization, R. Meziat, 2005.

  26. Convex envelopes 2D The convex envelope can be defined as: Caratheodory theorem: Every point in a convex envelope of a coercive function f can be expressed as a convex combination that has r+1 points when the function is defined in And we define the probability distribution supported in t1, …,tn with weights 1,…, n, Bidimensional polynomial: The method of moments in dynamic optimization, R. Meziat, 2005.

  27. Convex envelopes 2D For a fourth order polynomial: Where: We compute the convex envelope in (a,b) solving the SPD program with m10=a , m01 =b and m00=1 M: Restriction matrix that characterize the moments The method of moments in dynamic optimization, R. Meziat, 2005.

  28. We change the problem by a semidefinite program. The solution of the semidefinite program has the moments of the optimal measure. Convex envelope calculus in (a,b) We take marginal moments YES NO NO YES CASE I CASE II CASE III Convex envelopes 2D The method of moments in dynamic optimization, R. Meziat, 2005.

  29. Convex envelopes 2D • Weights: CASE I: • Support: tx=a, ty=b • Weight: =1 CASE II: • Support: tx=roots(P(tX)), ty=roots(P(ty)) The method of moments in dynamic optimization, R. Meziat, 2005.

  30. Convex envelopes 2D CASE III: • Support: tx=roots(P(tX)), ty=roots(P(ty)) • Weights: The method of moments in dynamic optimization, R. Meziat, 2005.

  31. Convex envelopes 2D Example 1: The method of moments in dynamic optimization, R. Meziat, 2005.

  32. Convex envelopes 2D Example 1: The method of moments in dynamic optimization, R. Meziat, 2005.

  33. Convex envelopes 2D We construct the measure for the polynomial f(x,y) in the point (0.5,0) Marginal measures: Jointed measure: The method of moments in dynamic optimization, R. Meziat, 2005.

  34. Convex envelopes 2D We construct the measure for the polynomial f(x,y) in the point (0,0.1) Marginal measures: Jointed measure: The method of moments in dynamic optimization, R. Meziat, 2005.

  35. Non linear, optimal control problems with Bolza form or Mayer form: Optimal control BOLZA FORM MAYER FORM The method of moments in dynamic optimization, R. Meziat, 2005.

  36. Optimal control • Convexity problems: • 2. NON CONVEX : • The Classical Theory of Optimal Control does not apply for proving the existence of the solution • Search Routines of Numerical Optimization fail to attain the global optimum. Linearity problems: 1. NON LINEAR: • Integration. • Stability • Chaos The method of moments in dynamic optimization, R. Meziat, 2005.

  37. Optimal control The Hamiltonian H has a polynomial form: We introduce the linear and convex relaxation with moments. The global optimization of a polynomial: m: New variable of control We use the probability moments The method of moments in dynamic optimization, R. Meziat, 2005.

  38. Optimal control Theorem: Assume that the Hamiltonian is a coercive polynomial with a single global minimum u*, then the optimization problem has an unique solution given by the Dirac measure u* Theorem: Let H(u) be an even degree algebraic polynomial whose leader coefficient k is positive, we can express its convex hull as: The method of moments in dynamic optimization, R. Meziat, 2005.

  39. Optimal control When H(u) is a coercive polynomial with a single global minimum u*, the solution is the vector of moments m. Hankel Positive semi definite The method of moments in dynamic optimization, R. Meziat, 2005.

  40. Discretization of the problem. Optimal control The method of moments in dynamic optimization, R. Meziat, 2005.

  41. Example 1: Optimal control t vs X Control signal The method of moments in dynamic optimization, R. Meziat, 2005.

  42. Optimal control The method of moments in dynamic optimization, R. Meziat, 2005.

  43. Example 2: Optimal control Control signal t vs X The method of moments in dynamic optimization, R. Meziat, 2005.

  44. Optimal control Example 3: t vs X t vs Y Control signal The method of moments in dynamic optimization, R. Meziat, 2005.

  45. Optimal control Example 4: THERE IS NO MINIMIZERS The method of moments in dynamic optimization, R. Meziat, 2005.

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