Date: 2026-05-01T06:12:08+00:00

Actual source code: armijo.h

      1: #pragma once

      3: /* Context for an Armijo (nonmonotone) linesearch for 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_ARMIJO;