robust optimization

Robustification of the k-Means Clustering Problem and Tailored Decomposition Methods: When More Conservative Means More Accurate

  • Jan Pablo Burgard
  • Carina Moreira Costa
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Robust Optimization Tags , , , ,

    k-means clustering is a classic method of unsupervised learning with the aim of partitioning a given number of measurements into k clusters. In many modern applications, however, this approach suffers from unstructured measurement errors because the k-means clustering result then represents a clustering of the erroneous measurements instead of retrieving the true underlying clustering structure. … Read more

    Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints

  • Dirk Lauinger
  • François Vuille
  • Daniel Kuhn
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering, Robust Optimization Tags , , , , ,

    Problem definition: Vehicle-to-grid increases the low utilization rate of privately owned electric vehicles by making their batteries available to electricity grids. We formulate a robust optimization problem that maximizes a vehicle owner’s expected profit from selling primary frequency regulation to the grid and guarantees that market commitments are met at all times for all frequency … Read more

    Gamma-Robust Electricity Market Equilibrium Models with Transmission and Generation Investments

  • Emre Çelebi
  • Vanessa Krebs
  • Martin Schmidt
    • Categories Applications - OR and Management Sciences, Robust Optimization Tags , , , ,

    We consider uncertain robust electricity market equilibrium problems including transmission and generation investments. Electricity market equilibrium modeling has a long tradition but is, in most of the cases, applied in a deterministic setting in which all data of the model are known. Whereas there exist some literature on stochastic equilibrium problems, the field of robust … Read more

    Dynamic Portfolio Selection with Linear Control Policies for Coherent Risk Minimization

  • Jun-ya Gotoh
  • Yuichi Takano
    • Categories Finance and Economics, Robust Optimization Tags , , ,

    This paper is concerned with a linear control policy for dynamic portfolio selection. We develop this policy by incorporating time-series behaviors of asset returns on the basis of coherent risk minimization. Analyzing the dual form of our optimization model, we demonstrate that the investment performance of linear control policies is directly connected to the intertemporal … Read more

    Gamma-Robust Linear Complementarity Problems with Ellipsoidal Uncertainty Sets

  • Vanessa Krebs
  • Martin Schmidt
  • Michael Müller
    • Categories Complementarity and Variational Inequalities, Robust Optimization, Transportation Tags , , ,

    We study uncertain linear complementarity problems (LCPs), i.e., problems in which the LCP vector q or the LCP matrix M may contain uncertain parameters. To this end, we use the concept of Gamma-robust optimization applied to the gap function formulation of the LCP. Thus, this work builds upon [16]. There, we studied Gamma-robustified LCPs for … Read more

    Robust Optimization with Decision-Dependent Information Discovery

  • Phebe Vayanos
  • Angelos Georghiou
  • Han Yu
    • Categories Robust Optimization Tags , , ,

    Robust optimization (RO) is a popular paradigm for modeling and solving two- and multi-stage decision-making problems affected by uncertainty. In many real-world applications, such as R&D project selection, production planning, or preference elicitation for product or policy recommendations, the time of information discovery is decision-dependent and the uncertain parameters only become observable after an often costly … Read more

    Robust Optimal Aiming Strategies in Concentrated Solar Tower Power Plants

  • Christina Büsing
  • Pascal Richter
  • Jeff Cumpston
  • Fynn Kepp
  • Sascha Kuhnke
    • Categories Robust Optimization Tags , , , ,

    A concentrated solar tower power plant consists of a receiver mounted atop of a central tower and a field of movable mirrors called heliostats. The heliostats concentrate solar radiation onto the receiver where a fluid is heated to produce electricity in a conventional thermodynamic cycle. Aiming strategies are used to assign each heliostat to an … Read more

    Near-optimal Robust Bilevel Optimization

  • Miguel F. Anjos
  • Mathieu Besançon
  • Luce Brotcorne
    • Categories (Mixed) Integer Nonlinear Programming, Complementarity and Variational Inequalities, Robust Optimization Tags , , , , , ,

    Bilevel optimization studies problems where the optimal response to a second mathematical optimization problem is integrated in the constraints. Such structure arises in a variety of decision-making problems in areas such as market equilibria, policy design or product pricing. We introduce near-optimal robustness for bilevel problems, protecting the upper-level decision-maker from bounded rationality at the … Read more

    Adjustable Robust Optimization Reformulations of Two-Stage Worst-case Regret Minimization Problems

  • Erick Delage
  • Mehran Poursoltani
    • Categories Applications - OR and Management Sciences, Linear Programming, Robust Optimization Tags , , ,

    This paper explores the idea that two-stage worst-case regret minimization problems with either objective or right-hand side uncertainty can be reformulated as two-stage robust optimization problems and can therefore benefit from the solution schemes and theoretical knowledge that have been developed in the last decade for this class of problems. In particular, we identify conditions … Read more

    An exact algorithm for robust influence maximization

  • Giacomo Nannicini
  • Roberto Wolfler Calvo
  • Giorgio Sartor
  • Emiliano Traversi
    • Categories Branch and Cut Algorithms, Robust Optimization Tags , ,

    We propose a Branch-and-Cut algorithm for the robust influence maximization problem. The influence maximization problem aims to identify, in a social network, a set of given cardinality comprising actors that are able to influence the maximum number of other actors. We assume that the social network is given in the form of a graph with … Read more