Semi-infinite Programming

Volumetric barrier decomposition algorithms for two-stage stochastic linear semi-infinite programming

  • Lewa' Alzaleq
  • Baha Alzalg
  • Asma Gafour
    • Categories Linear Programming, Semi-infinite Programming, Stochastic Programming Tags , , , ,

    In this paper, we study the two-stage stochastic linear semi-infinite programming with recourse to handle uncertainty in data defining (deterministic) linear semi-infinite programming. We develop and analyze volumetric barrier decomposition-based interior point methods for solving this class of optimization problems, and present a complexity analysis of the proposed algorithms. We establish our convergence analysis by … Read more

    An oracle-based projection and rescaling algorithm for linear semi-infinite feasibility problems and its application to SDP and SOCP

  • Tomonari Kitahara
  • Bruno F. Lourenço
  • Masakazu Muramatsu
  • Takashi Tsuchiya
  • Takayuki Okuno
    • Categories Linear, Cone and Semidefinite Programming, Semi-infinite Programming Tags , , ,

    We point out that Chubanov’s oracle-based algorithm for linear programming [5] can be applied almost as it is to linear semi-infinite programming (LSIP). In this note, we describe the details and prove the polynomial complexity of the algorithm based on the real computation model proposed by Blum, Shub and Smale (the BSS model) which is … Read more

    Robust-to-Dynamics Optimization

  • Amir Ali Ahmadi
  • Oktay Gunluk
    • Categories Convex Optimization, Robust Optimization, Semi-infinite Programming Tags , , ,

    A robust-to-dynamics optimization (RDO) problem} is an optimization problem specified by two pieces of input: (i) a mathematical program (an objective function $f:\mathbb{R}^n\rightarrow\mathbb{R}$ and a feasible set $\Omega\subseteq\mathbb{R}^n$), and (ii) a dynamical system (a map $g:\mathbb{R}^n\rightarrow\mathbb{R}^n$). Its goal is to minimize $f$ over the set $\mathcal{S}\subseteq\Omega$ of initial conditions that forever remain in $\Omega$ under … Read more

    Discretization-based algorithms for generalized semi-infinite and bilevel programs with coupling equality constraints

  • Hatim Djelassi
  • Alexander Mitsos
  • Moll Glass
    • Categories Global Optimization Applications, Global Optimization Theory, Semi-infinite Programming

    Discretization-based algorithms are proposed for the global solution of mixed-integer nonlinear generalized semi-infinite (GSIP) and bilevel (BLP) programs with lower-level equality constraints coupling the lower and upper level. The algorithms are extensions, respectively, of the algorithm proposed by Mitsos and Tsoukalas (J Glob Optim 61(1):1–17, 2015. https://doi.org/10.1007/s10898-014-0146-6) and by Mitsos (J Glob Optim 47(4):557–582, 2010. … Read more

    A transformation-based discretization method for solving general semi-infinite optimization problems

  • Karl-Heinz Küfer
  • Jan Schwientek
  • Tobias Seidel
    • Categories Semi-infinite Programming Tags , , , ,

    Discretization methods are commonly used for solving standard semi-infinite optimization (SIP) problems. The transfer of these methods to the case of general semi-infinite optimization (GSIP) problems is difficult due to the $x$-dependence of the infinite index set. On the other hand, under suitable conditions, a GSIP problem can be transformed into a SIP problem. In … Read more

    Revisiting Approximate Linear Programming Using a Saddle Point Approach

  • Qihang Lin
  • Selvaprabu Nadarajah
  • Negar Soheili
    • Categories Dynamic Programming, Semi-infinite Programming, Stochastic Programming Tags , , , , ,

    Approximate linear programs (ALPs) are well-known models for computing value function approximations (VFAs) of intractable Markov decision processes (MDPs) arising in applications. VFAs from ALPs have desirable theoretical properties, define an operating policy, and provide a lower bound on the optimal policy cost, which can be used to assess the suboptimality of heuristic policies. However, … Read more

    Decomposition Algorithms for Distributionally Robust Optimization using Wasserstein Metric

  • Fengqiao Luo
  • Sanjay Mehrotra
    • Categories Data-Mining, Robust Optimization, Semi-infinite Programming Tags , , , ,

    We study distributionally robust optimization (DRO) problems where the ambiguity set is de ned using the Wasserstein metric. We show that this class of DRO problems can be reformulated as semi-in nite programs. We give an exchange method to solve the reformulated problem for the general nonlinear model, and a central cutting-surface method for the convex case, … Read more

    Optimal scenario generation and reduction in stochastic programming

  • René Henrion
  • Werner Romisch
    • Categories Semi-infinite Programming, Stochastic Programming Tags , , ,

    Scenarios are indispensable ingredients for the numerical solution of stochastic optimization problems. Earlier approaches for optimal scenario generation and reduction are based on stability arguments involving distances of probability measures. In this paper we review those ideas and suggest to make use of stability estimates based on distances containing minimal information, i.e., on data appearing … Read more

    The structure of the infinite models in integer programming

  • Amitabh Basu
  • Michele Conforti
  • Marco Di Summa
  • Joseph Paat
    • Categories (Mixed) Integer Linear Programming, Cutting Plane Approaches, Semi-infinite Programming

    The infinite models in integer programming can be described as the convex hull of some points or as the intersection of half-spaces derived from valid functions. In this paper we study the relationships between these two descriptions. Our results have implications for finite dimensional corner polyhedra. One consequence is that nonnegative continuous functions suffice to … Read more

    Moment methods in energy minimization: New bounds for Riesz minimal energy problems

  • David de Laat
    • Categories Convex Optimization, Semi-definite Programming, Semi-infinite Programming Tags , , , , , ,

    We use moment methods to construct a converging hierarchy of optimization problems to lower bound the ground state energy of interacting particle systems. We approximate the infinite dimensional optimization problems in this hierarchy by block diagonal semidefinite programs. For this we develop the necessary harmonic analysis for spaces consisting of subsets of another space, and … Read more