Joint work with Sven Leyffer (Argonne National Laboratory)and Emilie Wanufelle (University of Namur).Motivated by problems related to power systems analysis which give riseto nonconvex mixed integer nonlinear programming (MINLP) problems,we propose a global optimization method based on ideas and techniquesthat can be easily extended to handle a ...
Creator:
Sartenaer, Annick (Facultés Universitaires Notre Dame de la Paix (Namur))
Created:
2008-11-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Joint work with Evrim Acar, and Daniel M. Dunlavy(Sandia National Laboratories).Tensor decompositions (e.g., higher-order analogues of matrix decompositions) are powerful tools for data analysis. In particular, the CANDECOMP/PARAFAC (CP) model has proved useful in many applications such as chemometrics, signal processing, and web analysis. The p...
Creator:
Kolda, Tamara G. (Sandia National Laboratories)
Created:
2008-10-30
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Optimization of controls and parameters coming from realistic full-scale simulation requires enormous computational effort. To make such optimization practical requires optimal multilevel solvers and scalable parallel algorithms. Even in the case where such solvers and algorithms are well understood for the forward problem, adapting them to the ...
Creator:
Barker, Andrew T. (Lawrence Livermore National Laboratory)
Created:
2016-06-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Assimilation of observation data in meteorology and space weather consists of using these data to estimate the current state and the spatially and temporally distributed parameters of Numerical Weather Prediction (NWP) models, which are often fluid dynamical equations. The aim of the data assimilation is to provide wider monitoring of the weathe...
Creator:
Wang, Chunming (University of Southern California)
Created:
2016-03-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Many complex applications can be formulated as optimization problems constrained by partial differential equations (PDEs) with integer decision variables. Examples include the remediation of contaminated sites and the maximization of oil recovery; the design of next generation solar cells; the layout design of wind-farms; the design and control ...
Creator:
Leyffer, Sven (Argonne National Laboratory)
Created:
2016-06-08
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Optimization concepts will be reviewed with an eye on their proved orpotential application in Electronic Structure Calculationsand other Chemical Physics problems. We will discuss the role oftrust-region schemes, line searches, linearly and nonlinearlyconstrained optimization, Inexact Restoration and SQP methods and thetype of convergence theori...
Creator:
Martinez, José Mario (State University of Campinas (UNICAMP))
Created:
2008-10-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We discuss optimal low-rank approximation of matrices with non-negative entries, without the need of a regularization parameter. It will be shown that the standard SVD-approximation can be recovered via convex-optimization, which is why adding mild convex constraints often gives an optimal solution. Moreover, the issue of computability will be a...
Creator:
Grussler, Christian (Lund University)
Created:
2016-02-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We address the problem of optimally placing sensor networks for convection-diffusion processes where the convective part is perturbed. The problem is formulated as an optimal control problem where the integral Riccati equation is a constraint and the design variables are sensor locations. The objective functional involves a term associated to th...
Creator:
Rautenberg, Carlos Nicolà¡s (Humboldt-Universität)
Created:
2016-03-17
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Deep brain stimulation is a therapy where an electrode is placed in thebrain and periodic electrical pulses are delivered for treatment ofdiseases such as Parkinson's Disease and Epilepsy. This therapy hasbeen quite successful in Parkinson's and moderately successful inepilepsy. Despite the wide range of stimulation parameters that can beused, o...
Creator:
Netoff, Theoden (University of Minnesota, Twin Cities)
Created:
2015-11-11
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We survey some developments in machine learning and data analysis,focusing on those in which optimization is an importantcomponent. Some of these have possible relevance for industrial andenergy applications, for example, constraints and covariances could be'learned' from process data rather than specified a priori. Somepossibilities along these...
Creator:
Wright, Stephen (University of Wisconsin, Madison)
Created:
2016-02-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Parsimony, including sparsity and low rank, has been shown to successfully modeldata in numerous machine learning and signal processing tasks. Traditionally, suchmodeling approaches rely on an iterative algorithm that minimizes an objectivefunction with parsimony-promoting terms. The inherently sequential structure anddata-dependent complexity a...
Creator:
Sapiro, Guillermo R. (Duke University)
Created:
2013-09-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
PDE-constrained optimization is an essential technology for product development at The Boeing Company. This talk will survey three separate applications of increasing mathematical and computational complexity. The first application is a relatively straightforward parametric surface lofting application. The second application is structural analys...
Creator:
Grandine, Thomas (The Boeing Company)
Created:
2016-06-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This lecture is based on the following papers: 1. A. Majda and B. Gershgorin, 2010: Quantifying Uncertainty in Climate Change Science Through Empirical Information Theory, PNAS in press 2. A. Majda, R. Abramov, B. Gershgorin, 'High Skill in Low Frequency Climate Response through Fluctuation Dissipation Theorems Despite Structural Instability,' P...
Creator:
Majda, Andrew J. (New York University)
Created:
2010-10-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Target is a unique retailer with a large and complex supply chain network supporting a diverse set of SKUs that it offers in store across the US and online. Making this network efficient entails solving multiple interconnected optimization problems using techniques in stochastic modeling and optimization, algorithm development and scaling, and a...
Creator:
Khodjasteh, Kaveh (Target Corporation)
Created:
2018-12-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Mathematical models are employed ubiquitously for description, prediction and decision making. In addressing end-goal objectives, great care needs to be devoted to attainment of appropriate balance of inexactness throughout the various stages of the end goal process (e.g. modeling and optimization). Disregard to such considerations, either entai...
Creator:
Horesh, Lior (IBM)
Created:
2016-02-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We introduce a sparse time-frequency analysis method for analyzing nonlinear and non-stationary data. This method is inspired by the Empirical Mode Decomposition method (EMD) and the recently developed compressed sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary con...
Creator:
Hou, Thomas Y. (California Institute of Technology)
Created:
2011-09-08
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Modern commercial airplanes are assembled out of millions of different parts. While many of these parts are rigid, many of them are not. For example, the hydraulic lines and flexible electrical conduits that supply an airplane's landing gear change their shape as the landing gear goes through its motion (you can see some of these lines in the ac...
Creator:
Grandine, Thomas (The Boeing Company)
Created:
2011-08-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
One of the main developments in optimization over the last 20 years isSemi-Definite Programming. It treats problems which can be expressed as aLinear Matrix Inequality (LMI). Any such problem is necessarily convex,so the determining the scope and range of applicability comes down to thequestion:How much more restricted are LMIs than Convex Matri...
Creator:
Helton, J. William (University of California, San Diego)
Created:
2015-11-05
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.