Date: 2015-10-02T22:31:00+00:00

Actual source code: ssls.h
petsc-3.6.2 2015-10-02
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      1: /* Context for SSXLS
      2:    -- semismooth (SS) - function not differentiable
      3:                       - merit function continuously differentiable
      4:                       - Fischer-Burmeister reformulation of complementarity
      5:                         - Billups composition for two finite bounds
      6:    -- infeasible (I)  - iterates not guaranteed to remain within bounds
      7:    -- feasible (F)    - iterates guaranteed to remain within bounds
      8:    -- linesearch (LS) - Armijo rule on direction

     10:  Many other reformulations are possible and combinations of
     11:  feasible/infeasible and linesearch/trust region are possible.

     13:  Basic theory
     14:    Fischer-Burmeister reformulation is semismooth with a continuously
     15:    differentiable merit function and strongly semismooth if the F has
     16:    lipschitz continuous derivatives.

     18:    Every accumulation point generated by the algorithm is a stationary
     19:    point for the merit function.  Stationary points of the merit function
     20:    are solutions of the complementarity problem if
     21:      a.  the stationary point has a BD-regular subdifferential, or
     22:      b.  the Schur complement F'/F'_ff is a P_0-matrix where ff is the
     23:          index set corresponding to the free variables.

     25:    If one of the accumulation points has a BD-regular subdifferential then
     26:      a.  the entire sequence converges to this accumulation point at
     27:          a local q-superlinear rate
     28:      b.  if in addition the reformulation is strongly semismooth near
     29:          this accumulation point, then the algorithm converges at a
     30:          local q-quadratic rate.

     32:  The theory for the feasible version follows from the feasible descent
     33:  algorithm framework.

     35:  References:
     36:    Billups, "Algorithms for Complementarity Problems and Generalized
     37:      Equations," Ph.D thesis, University of Wisconsin - Madison, 1995.
     38:    De Luca, Facchinei, Kanzow, "A Semismooth Equation Approach to the
     39:      Solution of Nonlinear Complementarity Problems," Mathematical
     40:      Programming, 75, pages 407-439, 1996.
     41:    Ferris, Kanzow, Munson, "Feasible Descent Algorithms for Mixed
     42:      Complementarity Problems," Mathematical Programming, 86,
     43:      pages 475-497, 1999.
     44:    Fischer, "A Special Newton-type Optimization Method," Optimization,
     45:      24, pages 269-284, 1992
     46:    Munson, Facchinei, Ferris, Fischer, Kanzow, "The Semismooth Algorithm
     47:      for Large Scale Complementarity Problems," Technical Report 99-06,
     48:      University of Wisconsin - Madison, 1999.
     49: */

     51: #ifndef __TAO_SSLS_H
     53: #include <petsc/private/taoimpl.h>

     55: typedef struct {
     56:   Vec ff;       /* fischer function */
     57:   Vec dpsi;     /* gradient of psi */

     59:   Vec da;       /* work vector for subdifferential calculation (diag pert) */
     60:   Vec db;       /* work vector for subdifferential calculation (row scale) */
     61:   Vec dm;   /* work vector for subdifferential calculation (mu vector) */
     62:   Vec dxfree;

     64:   Vec t1;       /* work vector */
     65:   Vec t2;       /* work vector */

     67:   Vec r1,r2,r3,w; /* work vectors */

     69:   PetscReal merit; /* merit function value (norm(fischer)) */
     70:   PetscReal merit_eqn;
     71:   PetscReal merit_mu;

     73:   PetscReal delta;
     74:   PetscReal rho;

     76:   PetscReal rtol;       /* Solution tolerances */
     77:   PetscReal atol;

     79:   PetscReal identifier; /* Active-set identification */

     81:   /* Interior-point method data */
     82:   PetscReal mu_init; /* initial smoothing parameter value */
     83:   PetscReal mu;      /* smoothing parameter */
     84:   PetscReal dmu;     /* direction in smoothing parameter */
     85:   PetscReal mucon;   /* smoothing parameter constraint */
     86:   PetscReal d_mucon; /* derivative of smoothing constraint with respect to mu */
     87:   PetscReal g_mucon; /* gradient of merit function with respect to mu */

     89:   Mat J_sub, Jpre_sub; /* subset of jacobian */
     90:   Vec f;        /* constraint function */

     92:   IS fixed;
     93:   IS free;
     94: } TAO_SSLS;

     96: PetscErrorCode TaoSetFromOptions_SSLS(PetscOptions *,Tao);
     97: PetscErrorCode TaoView_SSLS(Tao,PetscViewer);

     99: PetscErrorCode Tao_SSLS_Function(TaoLineSearch, Vec, PetscReal *, void *);
    100: PetscErrorCode Tao_SSLS_FunctionGradient(TaoLineSearch, Vec, PetscReal *, Vec, void *);

    102: #endif