KNITRO for Mathematica Products
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KNITRO for Mathematica
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Features

New in Version 5.2

  • Energy (management of distribution networks, optimal plant operations, revenue and risk management, and strategic pricing)
  • Finance (option pricing; portfolio optimization; optimal pricing; risk management; credit risk; strategic bidding and auctions, including Nash equilibrium, demand optimization, and nonlinear least-squares data fitting; volatility estimation; asset-liability management; and index tracking)
  • Manufacturing (CAD, optimal computer chip layout, and transportation networks)
  • Many other difficult, large-scale nonlinear problems

General Features

  • Solves large-scale general nonlinear programming (NLP) problems (continuous variables, smooth functions).
  • Solves large-scale linear programming (LP) problems.
  • Solves large-scale quadratic programming (QP) problems, both convex and nonconvex.
  • Solves large-scale nonlinear least squares problems, and nonlinear systems of equations.
  • Provides two state-of-the-art optimizer algorithms: interior-point (barrier) and active-set.
  • Solves small or large optimization problems efficiently and robustly:
    • Rapidly converges to a high-precision local solution using Newton-based methods.
    • Computes analytic derivatives from Mathematica's symbolic problem definition.
    • Linear algebra operations choose between iterative (conjugate gradient) and direct (sparse factorization) methods.
  • Provides special options for difficult or unusual problems:
    • Can require that every iterate remains feasible with respect to all inequality constraints.
    • Can cross over from the interior-point algorithm to the active-set one for final determination of a vertex solution.
  • Offers several choices for high-precision Newton-based solution methods:
    • Solve with Mathematica's analytic second derivatives.
    • Solve with finite difference approximation of second derivatives.
    • Solve with dense quasi-Newton approximations (BFGS and SR1).
    • Solve with limited-memory quasi-Newton approximation (L-BFGS).
    • Select your own starting point or let KNITRO for Mathematica compute one for you.


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