Applications – Science and Engineering

Approaches to iterative algorithms for solving nonlinear equations with an application in tomographic absorption spectroscopy

  • Francisco J. Aragón Artacho
  • Weiwei Cai
  • Yair Censor
  • Aviv Gibali
  • Chongyuan Shui
  • David Torregrosa-Belén
    • Categories Biomedical Applications, Constrained Nonlinear Optimization, Nonsmooth Optimization Tags , , , , , ,

    In this paper we propose an approach for solving systems of nonlinear equations without computing function derivatives. Motivated by the application area of tomographic absorption spectroscopy, which is a highly-nonlinear problem with variables coupling, we consider a situation where straightforward translation to a fixed point problem is not possible because the operators that represent the … Read more

    Extended Formulations for Control Languages Defined by Finite-State Automata

  • Christoph Buchheim
  • Maximilian Merkert
    • Categories (Mixed) Integer Linear Programming, Optimization of Systems modeled by PDEs, Polyhedra Tags , , , ,

    Many discrete optimal control problems feature combinatorial constraints on the possible switching patterns, a common example being minimum dwell-time constraints. After discretizing to a finite time grid, for these and many similar types of constraints, it is possible to give a description of the convex hull of feasible (finite-dimensional) binary controls via extended formulations. In … Read more

    A Sequential Benders-based Mixed-Integer Quadratic Programming Algorithm

  • Andrea Ghezzi
  • Wim Van Roy
  • Sebastian Sager
  • Moritz Diehl
    • Categories (Mixed) Integer Nonlinear Programming, Applications - Science and Engineering, Optimization Software Benchmark Tags , , , ,

    For continuous decision spaces, nonlinear programs (NLPs) can be efficiently solved via sequential quadratic programming (SQP) and, more generally, sequential convex programming (SCP). These algorithms linearize only the nonlinear equality constraints and keep the outer convex structure of the problem intact, such as (conic) inequality constraints or convex objective terms. The aim of the presented … Read more

    Adjustable Robust Nonlinear Network Design Without Controllable Elements under Load Scenario Uncertainties

  • Johannes Thürauf
  • Julia Grübel
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Energy, Robust Optimization Tags , , , ,

    We study network design problems for nonlinear and nonconvex flow models without controllable elements under load scenario uncertainties, i.e., under uncertain injections and withdrawals. To this end, we apply the concept of adjustable robust optimization to compute a network design that admits a feasible transport for all, possibly infinitely many, load scenarios within a given … Read more

    Frequency regulation with storage: On losses and profits

  • Dirk Lauinger
  • François Vuille
  • Daniel Kuhn
    • Categories Energy, Robust Optimization, Semi-infinite Programming Tags , , , , , ,

    Low-carbon societies will need to store vast amounts of electricity to balance intermittent generation from wind and solar energy, for example, through frequency regulation. Here, we derive an analytical solution to the decision-making problem of storage operators who sell frequency regulation power to grid operators and trade electricity on day-ahead markets. Mathematically, we treat future … Read more

    Black-box optimization for the design of a jet plate for impingement cooling

  • Filippo Marini
  • Margherita Porcelli
  • Elisa Riccietti
  • Lorenzo Cocchi
    • Categories Applications - Science and Engineering, Optimization of Simulated Systems, Other Topics Tags , , ,

    In this work we show how exploiting black-box optimization in the design of a cooling system for a nozzle in a gas turbine. We develop a black-box function that simulates an impingement cooling system starting from a well-known model that correlates the design features of the cooling system with efficiency parameters. We also provide a … Read more

    Accurate and Warm-Startable Linear Cutting-Plane Relaxations for ACOPF

  • Daniel Bienstock
  • Matias Villagra
    • Categories Convex Optimization, Energy, Linear, Cone and Semidefinite Programming Tags , ,

    We present a linear cutting-plane relaxation approach that rapidly proves tight lower bounds for the Alternating Current Optimal Power Flow Problem (ACOPF). Our method leverages outer-envelope linear cuts for well-known second-order cone relaxations for ACOPF along with modern cut management techniques. These techniques prove effective on a broad family of ACOPF instances, including the largest … Read more

    Using Filter Methods to Guide Convergence for ADMM, with Applications to Nonnegative Matrix Factorization Problems

  • Robert Baraldi
  • Stefan M. Wild
  • Sven Leyffer
    • Categories Applications - Science and Engineering, Nonlinear Optimization Tags , , ,

    Nonconvex, nonlinear cost functions arise naturally in physical inverse problems and machine learning. The alternating direction method of multipliers (ADMM) has seen extensive use in these applications, despite exhibiting uncertain convergence behavior in many practical nonconvex settings, and struggling with general nonlinear constraints. In contrast, filter methods have proved effective in enforcing convergence for sequential … Read more

    Optimization Over Trained Neural Networks: Taking a Relaxing Walk

  • Jiatai Tong
  • Junyang Cai
  • Thiago Serra
    • Categories (Mixed) Integer Linear Programming, Data-Mining, Meta Heuristics

    Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compression, and optimization with learned constraints. However, solving these formulations soon becomes difficult as the network size grows due to the weak linear relaxation and dense constraint matrix. We have seen … Read more

    Design Guidelines for Noise-Tolerant Optimization with Applications in Robust Design

  • Yuchen Lou
  • Shigeng Sun
  • Jorge Nocedal
    • Categories Nonlinear Optimization, Optimization of Systems modeled by PDEs, Stochastic Programming

    The development of nonlinear optimization algorithms capable of performing reliably in the presence of noise has garnered considerable attention lately. This paper advocates for strategies to create noise-tolerant nonlinear optimization algorithms by adapting classical deterministic methods. These adaptations follow certain design guidelines described here, which make use of estimates of the noise level in the … Read more