stochastic optimization

Generalized Gradients in Problems of Dynamic Optimization, Optimal Control, and Machine Learning

  • Vladimir I. Norkin
    • Categories Nonsmooth Optimization, Stochastic Programming Tags , , , , , , ,

    In this work, nonconvex nonsmooth problems of dynamic optimization, optimal control in discrete time (including feedback control), and machine learning are considered from a common point of view. An analogy is observed between tasks of controlling discrete dynamic systems and training multilayer neural networks with nonsmooth target function and connections. Methods for calculating generalized gradients … Read more

    A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints

  • Qihang Lin
  • Selvaprabu Nadarajah
  • Negar Soheili
  • Tianbao Yang
    • Categories Convex Optimization, Stochastic Programming Tags , , ,

    Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solving such SOECs need to limit the fraction of data points that they use, which we refer … Read more

    Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system

  • Bismark Singh
  • Bernard Knueven
    • Categories Energy, Stochastic Programming Tags , , , , , , , ,

    Published in Journal of Global Optimization. https://doi.org/10.1007/s10898-021-01041-y We develop a stochastic optimization model for scheduling a hybrid solar-battery storage system. Solar power in excess of the promise can be used to charge the battery, while power short of the promise is met by discharging the battery. We ensure reliable operations by using a joint chance … Read more

    An Inexact Primal-Dual Smoothing Framework for Large-Scale Non-Bilinear Saddle Point Problems

  • William Haskell
  • Le Thi Khanh Hien
  • Renbo Zhao
    • Categories Convex Optimization Tags , , , ,

    We develop an inexact primal-dual first-order smoothing framework to solve a class of non-bilinear saddle point problems with primal strong convexity. Compared with existing methods, our framework yields a significant improvement over the primal oracle complexity, while it has competitive dual oracle complexity. In addition, we consider the situation where the primal-dual coupling term has … Read more

    A linearly convergent stochastic recursive gradient method for convex optimization

  • Tiande Guo
  • Yan Liu
  • Xiao Wang
    • Categories Convex and Nonsmooth Optimization, Convex Optimization Tags , , , ,

    The stochastic recursive gradient algorithm (SARAH) [8] attracts much interest recently. It admits a simple recursive framework for updating stochastic gradient estimates. Motivated by this, in this paper, we propose a SARAH-I method incorporating importance sampling, whose linear conver- gence rate of the sequence of distances between iterates and the optima set is proven under … Read more

    Admissibility of solution estimators for stochastic optimization

  • Amitabh Basu
  • Tu Nguyen
  • Ao Sun
    • Categories Stochastic Programming Tags , , ,

    We look at stochastic optimization problems through the lens of statistical decision theory. In particular, we address admissibility, in the statistical decision theory sense, of the natural sample average estimator for a stochastic optimization problem (which is also known as the empirical risk minimization (ERM) rule in learning literature). It is well known that for … Read more

    Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality, and Saddle-Points

  • Krishnakumar Balasubramanian
  • Saeed Ghadimi
    • Categories Nonlinear Optimization, Stochastic Programming Tags , , , , ,

    In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, high-dimensional setting, and saddle-point avoiding. To handle constrained optimization, we first propose generalizations of the conditional gradient algorithm achieving rates similar to the standard stochastic gradient algorithm using only zeroth-order information. To … Read more

    Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming

  • Saeed Ghadimi
  • Raymond Perkins
  • Warren B. Powell
    • Categories Applications - OR and Management Sciences, Optimization of Simulated Systems, Stochastic Programming Tags , , , ,

    The most common approaches for solving stochastic resource allocation problems in the research literature is to either use value functions (“dynamic programming”) or scenario trees (“stochastic programming”) to approximate the impact of a decision now on the future. By contrast, common industry practice is to use a deterministic approximation of the future which is easier … Read more

    Large-scale Influence Maximization via Maximal Covering Location

  • Evren Güney
  • Markus Leitner
  • Mario Ruthmair
  • Markus Sinnl
    • Categories Applications - OR and Management Sciences, Integer Programming, Stochastic Programming Tags , , ,

    Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based … Read more

    Data-Driven Maintenance and Operations Scheduling in Power Systems under Decision-Dependent Uncertainty

  • Shabbir Ahmed
  • Nagi Gebraeel
  • Beste Basciftci
    • Categories Applications - OR and Management Sciences, Applications - Science and Engineering Tags , , ,

    Generator maintenance scheduling plays a pivotal role in ensuring uncompromising operations of power systems. There exists a tight coupling between the condition of the generators and corresponding operational schedules, significantly affecting reliability of the system. In this study, we effectively model and solve an integrated condition-based maintenance and operations scheduling problem for a fleet of … Read more