Junyi Liu

Adaptive Importance Sampling Based Surrogation Methods for Bayesian Hierarchical Models, via Logarithmic Integral Optimization

  • Ziyu He
  • Junyi Liu
  • Jong-Shi Pang
    • Categories Constrained Nonlinear Optimization, Nonsmooth Optimization, Stochastic Programming Tags , ,

    We explore Maximum a Posteriori inference of Bayesian Hierarchical Models (BHMs) with intractable normalizers, which are increasingly prevalent in contemporary applications and pose computational challenges when combined with nonconvexity and nondifferentiability. To address these, we propose the Adaptive Importance Sampling-based Surrogation method, which efficiently handles nonconvexity and nondifferentiability while improving the sampling approximation of the … Read more

    Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty

  • Guangyu Li
  • Junyi Liu
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags ,

    Predictive analytics, empowered by machine learning, is usually followed by decision-making problems in prescriptive analytics. We extend the above sequential prediction-optimization paradigm to a coupled scheme such that the prediction model can guide the decision problem to produce coordinated decisions yielding higher levels of performance. Speci fically, for stochastic programming (SP) models with latently decision-dependent uncertainty, … Read more

    Two-stage Stochastic Programming with Linearly Bi-parameterized Quadratic Recourse

  • Ying Cui
  • Junyi Liu
  • Jong-Shi Pang
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , ,

    This paper studies the class of two-stage stochastic programs (SP) with a linearly bi-parameterized recourse function defined by a convex quadratic program. A distinguishing feature of this new class of stochastic programs is that the objective function in the second stage is linearly parameterized by the first-stage decision variable, in addition to the standard linear … Read more

    Asymptotic results of Stochastic Decomposition for Two-stage Stochastic Quadratic Programming

  • Junyi Liu
  • Suvrajeet Sen
    • Categories Convex and Nonsmooth Optimization, Stochastic Programming Tags , ,

    This paper presents stochastic decomposition (SD) algorithms for two classes of stochastic programming problems: 1) two-stage stochastic quadratic-linear programming (SQLP) in which a quadratic program defines the objective function in the first stage and a linear program defines the value function in the second stage; 2) two-stage stochastic quadratic-quadratic programming (SQQP) which has quadratic programming … Read more

    Coalescing Data and Decision Sciences for Analytics

  • Yunxiao Deng
  • Junyi Liu
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , ,

    The dream of analytics is to work from common, clean, and consistent data sources in a manner that all of its facets (descriptive, predictive, and prescriptive) are sup- ported via a coherent vision of data and decision sciences. To the extent that data and decisions sciences work within logically/mathematically consistent frameworks, and that these paradigms … Read more