Applications – Science and Engineering

Robust optimization of dose-volume metrics for prostate HDR-brachytherapy incorporating target- and OAR volume delineation uncertainties

  • Marleen Balvert
  • Dick den Hertog
  • Aswin Hoffmann
    • Categories Biomedical Applications, Robust Optimization Tags , , , ,

    In radiation therapy planning, uncertainties in target volume definition yield a risk of underdosing the tumor. The classical way to prevent this in the context of external beam radiotherapy (EBRT) has been to expand the clinical target volume (CTV) with an isotropic margin to obtain the planning target volume (PTV). However, the EBRT-based PTV concept … Read more

    Robust Nonparametric Testing for Causal Inference in Observational Studies

  • Md. Noor-E-Alam
  • Cynthia Rudin
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Data-Mining Tags , , , , ,

    We consider the decision problem of making causal conclusions from observational data. Typically, using standard matched pairs techniques, there is a source of uncertainty that is not usually quanti fied, namely the uncertainty due to the choice of the experimenter: two di fferent reasonable experimenters can easily have opposite results. In this work we present an alternative … Read more

    Application of the Laminar Navier-Stokes Equations for Solving 2D and 3D Pathfinding Problems with Static and Dynamic Spatial Constraints. Implementation and validation in Comsol Multiphysics.

  • Benjamin Ivorra
    • Categories Applications - Science and Engineering, Other Topics, Transportation

    Pathfinding problems consist in determining the optimal shortest path, or at least one path, between two points in the space. In this paper, we propose a particular approach, based on methods used in Computational Fluid Dynamics, that intends to solve such problems. In particular, we reformulate pathfinding problems as the motion of a viscous fluid … Read more

    GasLib – A Library of Gas Network Instances

  • Denis Aßmann
  • Robert Burlacu
  • Imke Joormann
  • Thorsten Koch
  • Marc E. Pfetsch
  • Lars Schewe
  • Martin Schmidt
  • Mathias Sirvent
  • Jesco Humpola
  • Nikolaos Kanelakis
  • Djamal Oucherif
  • Robert Schwarz
    • Categories (Mixed) Integer Nonlinear Programming, Applications - Science and Engineering Tags , , , ,

    The development of mathematical simulation and optimization models and algorithms for solving gas transport problems is an active field of research. In order to test and compare these models and algorithms, gas network instances together with demand data are needed. The goal of GasLib is to provide a set of publicly available gas network instances … Read more

    Sparse Recovery via Partial Regularization: Models, Theory and Algorithms

  • Xiaorui Li
  • Zhaosong Lu
    • Categories Nonlinear Optimization, Nonsmooth Optimization, Statistics Tags , , , , , , ,

    In the context of sparse recovery, it is known that most of existing regularizers such as $\ell_1$ suffer from some bias incurred by some leading entries (in magnitude) of the associated vector. To neutralize this bias, we propose a class of models with partial regularizers for recovering a sparse solution of a linear system. We … Read more

    Acceleration of the PDHGM on strongly convex subspaces

  • Thomas Pock
  • Tuomo Valkonen
    • Categories Applications - Science and Engineering, Convex Optimization, Generalized Convexity/Monoticity Tags , , ,

    We propose several variants of the primal-dual method due to Chambolle and Pock. Without requiring full strong convexity of the objective functions, our methods are accelerated on subspaces with strong convexity. This yields mixed rates, $O(1/N^2)$ with respect to initialisation and $O(1/N)$ with respect to the dual sequence, and the residual part of the primal … Read more

    Blessing of Massive Scale: Spatial Graphical Model Estimation with a Total Cardinality Constraint

  • Ethan Fang
  • Mengdi Wang
  • Han Liu
    • Categories Combinatorial Optimization, Statistics Tags , , , ,

    We consider the problem of estimating high dimensional spatial graphical models with a total cardinality constraint (i.e., the l0-constraint). Though this problem is highly nonconvex, we show that its primal-dual gap diminishes linearly with the dimensionality and provide a convex geometry justification of this ‘blessing of massive scale’ phenomenon. Motivated by this result, we propose … Read more

    Partial outer convexification for traffic light optimization in road networks

  • Simone Göttlich
  • Andreas Potschka
  • Ute Ziegler
    • Categories (Mixed) Integer Nonlinear Programming, Optimization of Systems modeled by PDEs, Transportation Tags , , , ,

    We consider the problem of computing optimal traffic light programs for urban road intersections using traffic flow conservation laws on networks. Based on a Partial Outer Convexification approach, which has been successfully applied in the area of mixed-integer optimal control for systems of ordinary or differential algebraic equations, we develop a computationally tractable two-stage solution … Read more

    A Benders Decomposition Approach for the Charging Station Location Problem with Plug-in Hybrid Electric Vehicles

  • Okan Arslan
  • Oya Ekin Karasan
    • Categories Cutting Plane Approaches, Facility Planning and Design, Network Optimization Tags , , , , ,

    The flow refueling location problem (FRLP) locates $p$ stations in order to maximize the flow volume that can be accommodated in a road network respecting the range limitations of the vehicles. This paper introduces the charging station location problem with plug-in hybrid electric vehicles (CSLP-PHEV) as a generalization of the FRLP. We consider not only … Read more

    An Extended Frank-Wolfe Method with “In-Face” Directions, and its Application to Low-Rank Matrix Completion

  • Robert M. Freund
  • Rahul Mazumder
  • Paul Grigas
    • Categories Convex Optimization, Data-Mining Tags , , , , ,

    We present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensional faces of the feasible region. We present computational guarantees for the method that trade off efficiency in computing near-optimal solutions with upper bounds on the dimension of minimal faces of iterates. We apply our method to the low-rank … Read more