Raghu Pasupathy

Uncertainty Quantification for Multiobjective Stochastic Convex Quadratic Programs

  • Ziyu Liu
  • Susan R. Hunter
  • Nan Kong
  • Raghu Pasupathy
    • Categories Multi-Criteria Optimization, Quadratic Programming, Stochastic Programming

    A multiobjective stochastic convex quadratic program (MOSCQP) is a multiobjective optimization problem with convex quadratic objectives that are observed with stochastic error. MOSCQP is a useful problem formulation arising, for example, in model calibration and nonlinear system identification when a single regression model combines data from multiple distinct sources, resulting in a multiobjective least squares … Read more

    An Adaptive Sequential Sample Average Approximation Framework for Solving Two-stage Stochastic Programs

  • Raghu Pasupathy
  • Yongjia Song
    • Categories Stochastic Programming Tags , ,

    We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose is organized into \emph{outer} and \emph{inner} iterations as follows: during each outer iteration, a sample-path problem is implicitly generated using a sample of observations or “scenarios,” and solved only \emph{imprecisely}, to within a … Read more

    Multi-objective Ranking and Selection: Optimal Sampling Laws and Tractable Approximations via SCORE

  • Eric A. Applegate
  • Guy Feldman
  • Susan R. Hunter
  • Raghu Pasupathy
    • Categories Multi-Criteria Optimization, Optimization of Simulated Systems

    Consider the multi-objective ranking and selection (MORS) problem in which we select the Pareto-optimal set from a finite set of systems evaluated on three or more stochastic objectives. Solving this problem is difficult because we must determine how to allocate a simulation budget among the systems to minimize the probability that any systems are misclassified. … Read more

    The Adaptive Sampling Gradient Method: Optimizing Smooth Functions with an Inexact Oracle

  • Fatemeh Hashemi
  • Raghu Pasupathy
  • Michael Taaffe
    • Categories Nonlinear Optimization, Optimization of Simulated Systems, Stochastic Programming Tags , ,

    Consider settings such as stochastic optimization where a smooth objective function $f$ is unknown but can be estimated with an \emph{inexact oracle} such as quasi-Monte Carlo (QMC) or numerical quadrature. The inexact oracle is assumed to yield function estimates having error that decays with increasing oracle effort. For solving such problems, we present the Adaptive … Read more

    On Sampling Rates in Simulation-Based Recursions

  • Soumyadip Ghosh
  • Peter Glynn
  • Fatemeh Hashemi
  • Raghu Pasupathy
    • Categories Nonlinear Optimization, Optimization of Simulated Systems, Stochastic Programming Tags , , , ,

    We consider the context of “simulation-based recursions,” that is, recursions that involve quantities needing to be estimated using a stochastic simulation. Examples include stochastic adaptations of fixed-point and gradient descent recursions obtained by replacing function and derivative values appearing within the recursion by their Monte Carlo counterparts. The primary motivating settings are Simulation Optimization and … Read more

    Stochastically Constrained Simulation Optimization On Integer-Ordered Spaces: The cgR-SPLINE Algorithm

  • Kalyani Nagaraj
  • Raghu Pasupathy
    • Categories Global Optimization, Integer Programming, Stochastic Programming Tags , ,

    We consider the problem of identifying the solution(s) to an optimization problem whose domain is a subset of the integer lattice, and whose objective and constraint functions can only be observed using a stochastic simulation. Such problems seem particularly prevalent (see www.simopt.org) within service systems having capacity or service-level constraints. We present cgR-SPLINE — a … Read more