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ucSolve

Solves unconstrained nonlinear optimization problems with simple bounds on the variables. The solver includes several of the most popular search step methods for unconstrained optimization.

Main features

  • Implements the Newton method, the quasi-Newton BFGS and inverse BFGS method, the quasi-Newton DFP and inverse DFP method, the Fletcher-Reeves and Polak-Ribiere conjugate-gradient method, and the Fletcher conjugate descent method.
     
  • The Newton and the quasi-Newton methods is using a subspace minimization technique to handle rank problem. The equations system is solved using singular value decomposition, i.e. only dense equation systems are solved. Large, sparse systems is better solved with other nonlinear Tomlab solvers, or with the conjugate gradient methods.
     
  • The quasi-Newton methods use safe guarding techniques to avoid rank problem in the updated matrix.
     
  • The line search used is a modified version of an algorithm by Fletcher: Practical Methods of Optimization, 1987.
     
  • Bound constraints are treated similar as described in Gill, Murray, Wright: Practical Optimization, Academic Press, 1982.

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