software

Unifying nonlinearly constrained nonconvex optimization

  • Charlie Vanaret
  • Sven Leyffer
    • Categories Constrained Nonlinear Optimization, Optimization Software Benchmark Tags , , , , , , , , ,

    Derivative-based iterative methods for nonlinearly constrained non-convex optimization usually share common algorithmic components, such as strategies for computing a descent direction and mechanisms that promote global convergence. Based on this observation, we introduce an abstract framework based on four common ingredients that describes most derivative-based iterative methods and unifies their workflows. We then present Uno, … Read more

    libDIPS — Discretization-Based Semi-Infinite and Bilevel Programming Solvers

  • Aron Zingler
  • Alexander Mitsos
  • Daniel Jungen
  • Hatim Djelassi
    • Categories Optimization Software and Modeling Systems, Robust Optimization, Semi-infinite Programming Tags , , , , ,

    We consider several hierarchical optimization programs: (generalized) semi-infinite and existence-constrained semi-infinite programs, minmax, and bilevel programs. Multiple adaptive discretization-based algorithms have been published for these program classes in recent decades. However, rigorous numerical performance comparisons between these algorithms are lacking. Indeed, if numerical comparisons are provided at all, they usually compare a small selection of … Read more

    PyMOSO: Software for Multi-Objective Simulation Optimization with R-PERLE and R-MinRLE

  • Kyle Cooper
  • Susan R. Hunter
    • Categories Optimization of Simulated Systems, Optimization Software and Modeling Systems Tags ,

    We present the PyMOSO software package for (1) solving multi-objective simulation optimization (MOSO) problems on integer lattices, and (2) implementing and testing new simulation optimization (SO) algorithms. First, for solving MOSO problems on integer lattices, PyMOSO implements R-PERLE, a state-of-the-art algorithm for two objectives, and R-MinRLE, a competitive benchmark algorithm for three or more objectives. … Read more

    Minotaur: A Mixed-Integer Nonlinear Optimization Toolkit

  • Sven Leyffer
  • Jeff Linderoth
  • James R. Luedtke
  • Ashutosh Mahajan
  • Todd S. Munson
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization, Optimization Software and Modeling Systems Tags , ,

    We present a flexible framework for general mixed-integer nonlinear programming (MINLP), called Minotaur, that enables both algorithm exploration and structure exploitation without compromising computational efficiency. This paper documents the concepts and classes in our framework and shows that our implementations of standard MINLP techniques are efficient compared with other state-of-the-art solvers. We then describe structure-exploiting … Read more

    PySP: Modeling and Solving Stochastic Programs in Python

  • William E. Hart
  • Jean-Paul Watson
  • David L. Woodruff
    • Categories Modeling Languages and Systems, Optimization Software Design Principles, Stochastic Programming Tags , , ,

    Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its wide-spread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic … Read more

    The state-of-the-art in conic optimization software

  • Hans D Mittelmann
    • Categories Linear, Cone and Semidefinite Programming, Optimization Software Benchmark Tags , ,

    This work gives an overview over the major codes available for the solution of linear semidefinite (SDP) and second-order cone (SOCP) programs. Some developments since the 7th DIMACS Challenge [9, 17] are pointed out as well as some currently under way. Instead of presenting per- formance tables, reference is made to the ongoing benchmark [19] … Read more

    On solving trust-region and other regularised subproblems in optimization

  • Sue Dollar
  • Nicholas I. M. Gould
  • Daniel P. Robinson
    • Categories Optimization Software and Modeling Systems, Quadratic Programming, Unconstrained Optimization Tags , ,

    The solution of trust-region and regularisation subproblems which arise in unconstrained optimization is considered. Building on the pioneering work of Gay, More’ and Sorensen, methods which obtain the solution of a sequence of parametrized linear systems by factorization are used. Enhancements using high-order polynomial approximation and inverse iteration ensure that the resulting method is both … Read more

    LANCELOt_simple, a simple interface to LANCELOT B

  • Nicholas I. M. Gould
  • Dominique Orban
  • Philippe L. Toint
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Optimization Software and Modeling Systems Tags , ,

    We describe LANCELOT_simple, an interface to the LANCELOT B nonlinear optimization package within the GALAHAD} library (Gould, Orban, Toint, 2003) which ignores problem structure. The result is an easy-to-use Fortran 90 subroutine, with a small number of intuitively interpretable arguments. However, since structure is ignored, the means of presenting problems to the solver limited and … Read more

    Knitro: An Integrated Package for Nonlinear Optimization

  • Richard H. Byrd
  • Jorge Nocedal
  • Richard A. Waltz
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems Tags , ,

    This paper describes Knitro 5.0, a C-package for nonlinear optimization that combines complementary approaches to nonlinear optimization to achieve robust performance over a wide range of application requirements. The package is designed for solving large-scale, smooth nonlinear programming problems, and it is also effective for the following special cases: unconstrained optimization, nonlinear systems of equations, … Read more

    SDPARA : SemiDefinite Programming Algorithm PARAllel Version

  • Katsuki Fujisawa
  • Masakazu Kojima
  • Makoto Yamashita
    • Categories Semi-definite Programming Tags , , , , ,

    Abstract: The SDPA (SemiDefinite Programming Algorithm) is known as efficient computer software based on primal-dual interior-point method for solving SDPs (Semidefinite Programs). In many applications, however, some SDPs become larger and larger, too large for the SDPA to solve on a single processor. In execution of the SDPA applied to large scale SDPs, the computation … Read more