# LBFGS

References: Byrd1995

Provides parameters to tune the limited memory BFGS (LBFGS) optimization [Edit on GitHub]

## Keywords

- MAX_F_PER_ITER

- MAX_H_RANK

- TRUST_RADIUS

- WANTED_PROJ_GRADIENT

- WANTED_REL_F_ERROR

## Keyword descriptions

MAX_F_PER_ITER: integer = 20

  Usage: MAX_F_PER_ITER {integer}

  Maximum number of force evaluations per iteration (used for the line search) [Edit on GitHub]

MAX_H_RANK: integer = 5

  Usage: MAX_H_RANK {integer}

  Maximum rank (and consequently size) of the approximate Hessian matrix used by the LBFGS optimizer. Larger values (e.g. 30) will accelerate the convergence behaviour at the cost of a larger memory consumption. [Edit on GitHub]

TRUST_RADIUS: real = -5.29177209E-001 [angstrom]

  Usage: TRUST_RADIUS {real}

  Trust radius used in LBFGS. Not completely in depth tested. Negativ values means no trust radius is used. [Edit on GitHub]

WANTED_PROJ_GRADIENT: real = 1.00000000E-016 [internal_cp2k]

  Usage: WANTED_PROJ_GRADIENT {real}

  Convergence criterion (overrides the general ones): Requested norm threshold of the gradient multiplied by the approximate Hessian. [Edit on GitHub]

WANTED_REL_F_ERROR: real = 1.00000000E-016 [internal_cp2k]

  Usage: WANTED_REL_F_ERROR {real}

  Convergence criterion (overrides the general ones): Requested relative error on the objective function of the optimizer (the energy) [Edit on GitHub]