Basic Sciences Applications

Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert

  • Ryan Cory-Wright
  • Cristina Cornelio
  • Sanjeeb Dash
  • Bachir El Khadir
  • Lior Horesh
    • Categories Basic Sciences Applications, Global Optimization Applications, Semi-definite Programming Tags , , ,

    The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science. Historically, scientists have derived natural laws by manipulating equations based on existing knowledge, forming new equations, and verifying them experimentally. In recent years, data-driven scientific discovery has emerged as a viable competitor in … Read more

    Switching Time Optimization for Binary Quantum Optimal Control

  • Xinyu Fei
  • Lucas Brady
  • Jeffrey Larson
  • Sven Leyffer
  • Siqian Shen
    • Categories (Mixed) Integer Nonlinear Programming, Basic Sciences Applications, Control Applications Tags , ,

    Quantum optimal control is a technique for controlling the evolution of a quantum system and has been applied to a wide range of problems in quantum physics. We study a binary quantum control optimization problem, where control decisions are binary-valued and the problem is solved in diverse quantum algorithms. In this paper, we utilize classical … Read more

    Polynomial argmin for recovery and approximation of multivariate discontinuous functions

  • Didier Henrion
  • Milan Korda
  • Jean-Bernard Lasserre
    • Categories Basic Sciences Applications, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , ,

    We propose to approximate a (possibly discontinuous) multivariate function f(x) on a compact set by the partial minimizer arg min_y p(x,y) of an appropriate polynomial p whose construction can be cast in a univariate sum of squares (SOS) framework, resulting in a highly structured convex semidefinite program. In a number of non-trivial cases (e.g. when … Read more

    Developments in mathematical algorithms and computational tools for proton CT and particle therapy treatment planning

  • Yair Censor
  • Keith E. Schubert
  • Reinhard W. Schulte
    • Categories Basic Sciences Applications, Biomedical Applications, Constrained Nonlinear Optimization Tags , , , , , , , ,

    We summarize recent results and ongoing activities in mathematical algorithms and computer science methods related to proton computed tomography (pCT) and intensitymodulated particle therapy (IMPT) treatment planning. Proton therapy necessitates a high level of delivery accuracy to exploit the selective targeting imparted by the Bragg peak. For this purpose, pCT utilizes the proton beam itself … Read more

    Decision Intelligence for Nationwide Ventilator Allocation

  • Jiajun Xu
  • Suvrajeet Sen
    • Categories Applications - OR and Management Sciences, Basic Sciences Applications, Optimization of Simulated Systems Tags , , , ,

    Many states in the U.S. have faced shortages of medical resources because of the surge in the number of patients suffering from COVID-19. As many projections indicate, the situation will be far worse in coming months. The upcoming challenge is not only due to the exponential growth in cases but also because of inherent uncertainty … Read more

    Finding the strongest stable massless column with a follower load and relocatable concentrated masses

  • Oleg Kirillov
  • Michael L. Overton
    • Categories Basic Sciences Applications, Nonsmooth Optimization Tags ,

    We consider the problem of optimal placement of concentrated masses along a massless elastic column that is clamped at one end and loaded by a nonconservative follower force at the free end. The goal is to find the largest possible interval such that the variation in the loading parameter within this interval preserves stability of … Read more

    Calmness of a perturbed Cournot Oligopoly Game with nonsmooth cost functions

  • Matthieu Maréchal
    • Categories Basic Sciences Applications Tags , ,

    This article deals with the calmness of a solution map of a Cournot Oligopoly Game with nonsmooth cost functions. The fact that the cost functions are not supposed to be differentiable allows for considering cases where some firms have diferent units of production, which have diferent marginal costs. In order to obtain results about the … Read more

    A Class of Smooth Exact Penalty Function Methods for Optimization Problems with Orthogonality Constraints

  • Nachuan Xiao
  • Xin Liu
  • Ya-xiang Yuan
    • Categories Basic Sciences Applications, Constrained Nonlinear Optimization Tags , ,

    Updating the augmented Lagrangian multiplier by closed-form expression yields efficient first-order infeasible approach for optimization problems with orthogonality constraints. Hence, parallelization becomes tractable in solving this type of problems. Inspired by this closed-form updating scheme, we propose an exact penalty function model with compact convex constraints (PenC). We show that PenC can act as an … Read more

    An algorithm for optimization with disjoint linear constraints and its application for predicting rain

  • Tijana Janjic
  • Philippe L. Toint
  • Yvonne Ruckstuhl
    • Categories Basic Sciences Applications, Quadratic Programming Tags ,

    A specialized algorithm for quadratic optimization (QO, or, formerly, QP) with disjoint linear constraints is presented. In the considered class of problems, a subset of variables are subject to linear equality constraints, while variables in a different subset are constrained to remain in a convex set. The proposed algorithm exploits the structure by combining steps … Read more

    Derivative-Free Superiorization: Principle and Algorithm

  • Yair Censor
  • Elias Salomão Helou
  • Edgar Garduño
  • Gabor T. Herman
    • Categories Basic Sciences Applications, Constrained Nonlinear Optimization, Convex and Nonsmooth Optimization

    The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based on an antipodal way of thinking to what leads to constrained minimization methods. Instead of adapting unconstrained minimization algorithms to handling constraints, it adapts feasibility-seeking algorithms to … Read more