1: #pragma once

  3: /* Context for an Armijo (nonmonotone) linesearch for orthant wise unconstrained
  4:  minimization.

  6:  Given a function f, the current iterate x, and a descent direction d:
  7:  Find the smallest i in 0, 1, 2, ..., such that:

  9:     f(x + (beta**i)d) <= f(x) + (sigma*beta**i)<grad f(x),d>

 11:  The nonmonotone modification of this linesearch replaces the f(x) term
 12:  with a reference value, R, and seeks to find the smallest i such that:

 14:     f(x + (beta**i)d) <= R + (sigma*beta**i)<grad f(x),d>

 16:  This modification does effect neither the convergence nor rate of
 17:  convergence of an algorithm when R is chosen appropriately.  Essentially,
 18:  R must decrease on average in some sense.  The benefit of a nonmonotone
 19:  linesearch is that local minimizers can be avoided (by allowing increase
 20:  in function value), and typically, fewer iterations are performed in
 21:  the main code.

 23:  The reference value is chosen based upon some historical information
 24:  consisting of function values for previous iterates.  The amount of
 25:  historical information used is determined by the memory size where the
 26:  memory is used to store the previous function values.  The memory is
 27:  initialized to alpha*f(x^0) for some alpha >= 1, with alpha=1 signifying
 28:  that we always force decrease from the initial point.

 30:  The reference value can be the maximum value in the memory or can be
 31:  chosen to provide some mean descent.  Elements are removed from the
 32:  memory with a replacement policy that either removes the oldest
 33:  value in the memory (FIFO), or the largest value in the memory (MRU).

 35:  Additionally, we can add a watchdog strategy to the search, which
 36:  essentially accepts small directions and only checks the nonmonotonic
 37:  descent criteria every m-steps.  This strategy is NOT implemented in
 38:  the code.

 40:  Finally, care must be taken when steepest descent directions are used.
 41:  For example, when the Newton direction does not satisfy a sufficient
 42:  descent criteria.  The code will apply the same test regardless of
 43:  the direction.  This type of search may not be appropriate for all
 44:  algorithms.  For example, when a gradient direction is used, we may
 45:  want to revert to the best point found and reset the memory so that
 46:  we stay in an appropriate level set after using a gradient steps.
 47:  This type of search is currently NOT supported by the code.

 49:  References:
 50: + * - Armijo, "Minimization of Functions Having Lipschitz Continuous
 51:     First-Partial Derivatives," Pacific Journal of Mathematics, volume 16,
 52:     pages 1-3, 1966.
 53: . * - Ferris and Lucidi, "Nonmonotone Stabilization Methods for Nonlinear
 54:     Equations," Journal of Optimization Theory and Applications, volume 81,
 55:     pages 53-71, 1994.
 56: . * - Grippo, Lampariello, and Lucidi, "A Nonmonotone Line Search Technique
 57:     for Newton's Method," SIAM Journal on Numerical Analysis, volume 23,
 58:     pages 707-716, 1986.
 59: - * - Grippo, Lampariello, and Lucidi, "A Class of Nonmonotone Stabilization
 60:     Methods in Unconstrained Optimization," Numerische Mathematik, volume 59,
 61:   pages 779-805, 1991. */
 62: #include <petsc/private/taolinesearchimpl.h>
 63: typedef struct {
 64:   PetscReal *memory;

 66:   PetscReal alpha;         /* Initial reference factor >= 1 */
 67:   PetscReal beta;          /* Steplength determination < 1 */
 68:   PetscReal beta_inf;      /* Steplength determination < 1 */
 69:   PetscReal sigma;         /* Acceptance criteria < 1) */
 70:   PetscReal minimumStep;   /* Minimum step size */
 71:   PetscReal lastReference; /* Reference value of last iteration */

 73:   PetscInt memorySize;        /* Number of functions kept in memory */
 74:   PetscInt current;           /* Current element for FIFO */
 75:   PetscInt referencePolicy;   /* Integer for reference calculation rule */
 76:   PetscInt replacementPolicy; /* Policy for replacing values in memory */

 78:   PetscBool nondescending;
 79:   PetscBool memorySetup;

 81:   Vec x; /* Maintain reference to variable vector to check for changes */
 82:   Vec work;
 83: } TaoLineSearch_OWARMIJO;

 85: static PetscErrorCode ProjWork_OWLQN(Vec w, Vec x, Vec gv, PetscReal *gdx);