Combinatorial simplex algorithms can solve mean payoff games

A combinatorial simplex algorithm is an instance of the simplex method in which the pivoting depends on combinatorial data only. We show that any algorithm of this kind admits a tropical analogue which can be used to solve mean payoff games. Moreover, any combinatorial simplex algorithm with a strongly polynomial complexity (the existence of such an algorithm is open) would provide in this way a strongly polynomial algorithm solving mean payoff games. Mean payoff games are known to be in NP and co-NP; whether they can be solved in polynomial time is an open problem. Our algorithm relies on a tropical implementation of the simplex method over a real closed field of Hahn series. One of the key ingredients is a new scheme for symbolic perturbation which allows us to lift an arbitrary mean payoff game instance into a non-degenerate linear program over Hahn series.Comment: v1: 15 pages, 3 figures; v2: improved presentation, introduction expanded, 18 pages, 3 figure

Averaging and linear programming in some singularly perturbed problems of optimal control

The paper aims at the development of an apparatus for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems (that is, problems of optimal control of SP systems) considered on the infinite time horizon. We mostly focus on problems with time discounting criteria but a possibility of the extension of results to periodic optimization problems is discussed as well. Our consideration is based on earlier results on averaging of SP control systems and on linear programming formulations of optimal control problems. The idea that we exploit is to first asymptotically approximate a given problem of optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional (ID) linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with two numerical examples.Comment: 53 pages, 10 figure

The Asymptotic Optimal Partition and Extensions of the Nonsubstitution Theorem

The data describing an asymptotic linear program rely on a single parameter, usually referred to as time, and unlike parametric linear programming, asymptotic linear programming is concerned with the steady state behavior as time increases to infinity. The fundamental result of this work shows that the optimal partition for an asymptotic linear program attains a steady state for a large class of functions. Consequently, this allows us to define an asymptotic center solution. We show that this solution inherits the analytic properties of the functions used to describe the feasible region. Moreover, our results allow significant extensions of an economics result known as the Nonsubstitution Theorem

The Asymptotic Optimal Partition and Extensions of the Nonsubstitution Theorem

The data describing an asymptotic linear program rely on a single parameter, usually referred to as time, and unlike parametric linear programming, asymptotic linear programming is concerned with the steady state behavior as time increases to infinity. The fundamental result of this work shows that the optimal partition for an asymptotic linear program attains a steady state for a large class of functions. Consequently, this allows us to define an asymptotic center solution. We show that this solution inherits the analytic properties of the functions used to describe the feasible region. Moreover, our results allow significant extensions of an economics result known as the Nonsubstitution Theorem

Algorithms for uniform optimal strategies in two-player zero-sum stochastic games with perfect information

In stochastic games with perfect information, in each state at most one player has more than one action available. We propose two algorithms which find the uniform optimal strategies for zero-sum two-player stochastic games with perfect information. Such strategies are optimal for the long term average criterion as well. We prove the convergence for one algorithm, which presents a higher complexity than the other one, for which we provide numerical analysis.Dans les jeux stochastiques à information parfaite, dans chaque etat, au plus, un joueur a plus d'une action disponibles. Nous proposons deux algorithmes qui trouvent les stratégies uniformément optimales pour les jeux stochastiques à somme nulle avec deux joueurs et information parfaite. Ces stratégies sont aussi optimales pour le critère de la moyenne à long terme. Nous prouvons la convergence pour un algorithme, qui a une plus grande complexité que l'autre, pour lequel nous offrons une analyse numérique

Tropicalizing the simplex algorithm

We develop a tropical analog of the simplex algorithm for linear programming. In particular, we obtain a combinatorial algorithm to perform one tropical pivoting step, including the computation of reduced costs, in O(n(m+n)) time, where m is the number of constraints and n is the dimension.Comment: v1: 35 pages, 7 figures, 4 algorithms; v2: improved presentation, 39 pages, 9 figures, 4 algorithm

Recent contributions to linear semi-infinite optimization: an update

This paper reviews the state-of-the-art in the theory of deterministic and uncertain linear semi-infinite optimization, presents some numerical approaches to this type of problems, and describes a selection of recent applications in a variety of fields. Extensions to related optimization areas, as convex semi-infinite optimization, linear infinite optimization, and multi-objective linear semi-infinite optimization, are also commented.This is an updated version of the paper “Recent contributions to linear semi-infinite optimization” that appeared in 4OR, 15(3), 221–264 (2017). It was supported by the MINECO of Spain and ERDF of EU, Grant MTM2014-59179-C2-1-P, and by the Australian Research Council, Project DP160100854

Mini-Workshop: Applied Koopmanism

Koopman and Perron–Frobenius operators are linear operators that encapsulate dynamics of nonlinear dynamical systems without loss of information. This is accomplished by embedding the dynamics into a larger infinite-dimensional space where the focus of study is shifted from trajectory curves to measurement functions evaluated along trajectories and densities of trajectories evolving in time. Operator-theoretic approach to dynamics shares many features with an optimization technique: the Lasserre moment–sums-of-squares (SOS) hierarchies, which was developed for numerically solving non-convex optimization problems with semialgebraic data. This technique embeds the optimization problem into a larger primal semidefinite programming (SDP) problem consisting of measure optimization over the set of globally optimal solutions, where measures are manipulated through their truncated moment sequences. The dual SDP problem uses SOS representations to certify bounds on the global optimum. This workshop highlighted the common threads between the operator-theoretic dynamical systems and moment–SOS hierarchies in optimization and explored the future directions where the synergy of the two techniques could yield results in fluid dynamics, control theory, optimization, and spectral theory

Newton-Raphson Consensus for Distributed Convex Optimization

We address the problem of distributed uncon- strained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints, and wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proved, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton- Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.Comment: 18 pages, preprint with proof

Abstract book

Welcome at the International Conference on Differential and Difference Equations & Applications 2015. The main aim of this conference is to promote, encourage, cooperate, and bring together researchers in the fields of differential and difference equations. All areas of differential & difference equations will be represented with special emphasis on applications. It will be mathematically enriching and socially exciting event. List of registered participants consists of 169 persons from 45 countries. The five-day scientific program runs from May 18 (Monday) till May 22, 2015 (Friday). It consists of invited lectures (plenary lectures and invited lectures in sections) and contributed talks in the following areas: Ordinary differential equations, Partial differential equations, Numerical methods and applications, other topics