hidden markov models

A Parametric Approach for Solving Convex Quadratic Optimization with Indicators Over Trees

  • Aaresh Bhathena
  • Salar Fattahi
  • Andrés Gómez
  • Simge Küçükyavuz
    • Categories (Mixed) Integer Nonlinear Programming, Dynamic Programming, Quadratic Programming Tags , , , , ,

    This paper investigates convex quadratic optimization problems involving $n$ indicator variables, each associated with a continuous variable, particularly focusing on scenarios where the matrix $Q$ defining the quadratic term is positive definite and its sparsity pattern corresponds to the adjacency matrix of a tree graph. We introduce a graph-based dynamic programming algorithm that solves this … Read more

    A Data-Driven Approach for a Class of Stochastic Dynamic Optimization Problems

  • Tito Homem-de-Mello
  • Thuener Silva
  • Davi Valladão
    • Categories Stochastic Programming Tags , , ,

    Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and … Read more

    A Low-Memory Approach For Best-State Estimation Of Hidden Markov Models With Model Error

  • Mihai Anitescu
  • Emil M. Constantinescu
  • Xiaoyan Zeng
    • Categories Civil and Environmental Engineering, Statistics, Unconstrained Optimization Tags , , , ,

    We present a low-memory approach for the best-state estimate (data assimilation) of hidden Markov models where model error is considered. In particular, our findings apply for the 4D- Var framework. The novelty of our approach resides in the fact that the storage needed by our estimation framework, while including model error, is dramatically reduced from … Read more