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 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.