Now showing items 1-10 of 10

    • Algorithms and Software for Convex Mixed Integer Nonlinear Programs 

      Bonami, Pierre; Kilinc, Mustafa; Linderoth, Jeff (University of Wisconsin-Madison Department of Computer Sciences, 2009)
      This paper provides a survey of recent progress and software for solving mixed integer nonlinear programs (MINLP) wherein the objective and constraints are defined by convex functions and integrality restrictions are ...
    • Effective Separation of Disjunctive Cuts for Convex Mixed Integer Nonlinear Programs 

      Kilinc, Mustafa; Linderoth, Jeff; Luedtke, James (University of Wisconsin-Madison Department of Computer Sciences, 2010)
      We describe a computationally effective method for generating disjunctive inequalities for convex mixed-integer nonlinear programs (MINLPs). The method relies on solving a sequence of cut-generating linear programs, ...
    • Encoding Optimal Customized Coverage Instrumentation 

      Ohmann, Peter; Brown, David Bingham; Neelakandan, Naveen; Linderoth, Jeff; Liblit, Ben (2016-08-26)
      Program coverage is an important software quality metric. Coverage is most commonly gathered in the testing lab during development. However, developers also sometimes use inexpensive forms of program coverage in ...
    • FATCOP 2.0: Advanced Features in an Opportunistic Mixed Integer Programming Solver 

      Linderoth, Jeff; Ferris, Michael; Chen, Qun (2000)
      We describe FATCOP 2.0, a new parallel mixed integer program solver that works in an opportunistic computing environment provided by the Condor resource management system. We outline changes to the search strategy of ...
    • Locally Ideal Formulations for Piecewise Linear Functions with Indicator Variables 

      Luedtke, Jim; Linderoth, Jeff; Sridhar, Srikrishna (2013-03-28)
      In this paper, we consider mixed integer linear programming (MIP) formulations for piecewise linear functions (PLFs) that are evaluated when an indicator variable is turned on. We describe modifications to standard MIP ...
    • Locally Ideal Formulations for Piecewise Linear Functions with Indicator Variables 

      Linderoth, Jeff; Luedtke, James; Sridhar, Srikrishna (2013-04-29)
      In this paper, we consider mixed integer linear programming (MIP) formulations for piecewise linear functions (PLFs) that are evaluated when an indicator variable is turned on. We describe modifications to standard MIP ...
    • Orbital Branching 

      Ostrowski, James; Linderoth, Jeff; Rossi, Fabrizio; Smriglio, Stefano (University of Wisconsin-Madison Department of Computer Sciences, 2008)
      We introduce
    • Solving Large Steiner Triple Covering Problems 

      Ostrowski, Jim; Linderoth, Jeff; Rossi, Fabrizio; Smriglio, Stefano (University of Wisconsin-Madison Department of Computer Sciences, 2009)
      Computing the 1-width of the incidence matrix of a Steiner Triple System gives rise to small set covering instances that provide a computational challenge for integer programming techniques. One major source of difficulty ...
    • Some Results on the Strength of Relaxations of Multilinear Functions\ 

      Luedtke, James; Namazifar, Mahdi; Linderoth, Jeff (University of Wisconsin-Madison Department of Computer Sciences, 2010)
      We study approaches for obtaining convex relaxations of global optimization problems containing multilinear functions. Specifically, we compare the concave and convex envelopes of these functions with the relaxations ...
    • Valid Inequalities for the Pooling Problem with Binary Variables 

      D'Ambrosio, Claudio; Linderoth, Jeff; Luedtke, James (University of Wisconsin-Madison Department of Computer Sciences, 2010)
      The pooling problem consists of finding the optimal quantity of final products to obtain by blending different compositions of raw materials in pools. Bilinear terms are required to model the quality of products in the ...