Dávid Papp

Interior-point algorithms with full Newton steps for nonsymmetric convex conic optimization

  • Dávid Papp
  • Anita Varga
    • Categories Cone Programming, Convex Optimization Tags , , ,

    We design and analyze primal-dual, feasible interior-point algorithms (IPAs) employing full Newton steps to solve convex optimization problems in standard conic form. Unlike most nonsymmetric cone programming methods, the algorithms presented in this paper require only a logarithmically homogeneous self-concordant barrier (LHSCB) of the primal cone, but compute feasible and \(\varepsilon\)-optimal solutions to both the … Read more

    Semi-infinite programming using high-degree polynomial interpolants and semidefinite programming

  • Dávid Papp
    • Categories Semi-definite Programming, Semi-infinite Programming, Statistics Tags , , , ,

    In a common formulation of semi-infinite programs, the infinite constraint set is a requirement that a function parametrized by the decision variables is nonnegative over an interval. If this function is sufficiently closely approximable by a polynomial or a rational function, then the semi-infinite program can be reformulated as an equivalent semidefinite program. Solving this … Read more

    A cutting surface algorithm for semi-infinite convex programming with an application to moment robust optimization

  • Sanjay Mehrotra
  • Dávid Papp
    • Categories Robust Optimization, Semi-infinite Programming, Stochastic Programming Tags , , , , , , ,

    We first present and analyze a central cutting surface algorithm for general semi-infinite convex optimization problems, and use it to develop an algorithm for distributionally robust optimization problems in which the uncertainty set consists of probability distributions with given bounds on their moments. The cutting surface algorithm is also applicable to problems with non-differentiable semi-infinite … Read more

    Generating moment matching scenarios using optimization techniques

  • Sanjay Mehrotra
  • Dávid Papp
    • Categories Stochastic Programming Tags , ,

    An optimization based method is proposed to generate moment matching scenarios for numerical integration and its use in stochastic programming. The main advantage of the method is its flexibility: it can generate scenarios matching any prescribed set of moments of the underlying distribution rather than matching all moments up to a certain order, and the … Read more

    Scenario decomposition of risk-averse multistage stochastic programming problems

  • Ricardo A. Collado
  • Dávid Papp
  • Andrzej Ruszczyński
    • Categories Stochastic Programming Tags , , ,

    For a risk-averse multistage stochastic optimization problem with a finite scenario tree, we introduce a new scenario decomposition method and we prove its convergence. The method is applied to a risk-averse inventory and assembly problem. In addition, we develop a partially regularized bundle method for nonsmooth optimization. CitationRUTCOR, Rutgers University, Piscataway, NJ 08854ArticleDownload View PDF

    Extension of the semidefinite characterization of sum of squares functional systems to algebraic structures

  • Farid Alizadeh
  • Ricardo A. Collado
  • Dávid Papp
    • Categories Convex Optimization, Semi-definite Programming Tags , , , , ,

    We extend Nesterov’s semidefinite programming (SDP) characterization of the cone of functions that can be expressed as sums of squares (SOS) of functions in finite dimensional linear functional spaces. Our extension is to algebraic systems that are endowed with a binary operation which map two elements of a finite dimensional vector space to another vector … Read more