Section XALMO_OPTIMIZER_TRUSTR

• Controls the trust-region optimization of extended ALMOs. Trust radius is varied in the outer loop. Once the trust radius is chosen (and fixed) the model function can be minized using various approaches. Currently, an iterative conjugate-gradient approach is used and controlled by the inner loop  [Edit on GitHub]

• Section path: CP2K_INPUT / FORCE_EVAL / DFT / ALMO_SCF / XALMO_OPTIMIZER_TRUSTR

• This section cannot be repeated.

• Subsections

  • none

• Keywords

  • ALGORITHM
  • CONJUGATOR
  • EPS_ERROR
  • EPS_ERROR_EARLY
  • ETA
  • INITIAL_TRUST_RADIUS
  • MAX_ITER
  • MAX_ITER_EARLY
  • MAX_ITER_OUTER_LOOP
  • MAX_TRUST_RADIUS
  • MODEL_GRAD_NORM_RATIO
  • PRECONDITIONER

• Keyword descriptions

  ALGORITHM
  	ALGORITHM {Keyword}
  	Selects an algorithm to solve the fixed-radius subproblem  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one keyword.
  	Default value: CAUCHY
  	List of valid keywords: CAUCHY Compute simple Cauchy point CG Steihaug's iterative CG algorithm that does not invert model Hessian DOGLEG Dogleg optimizer

  CONJUGATOR
  	CONJUGATOR {Keyword}
  	Various methods to compute step directions in the PCG optimization  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one keyword.
  	Default value: HAGER_ZHANG
  	List of valid keywords: DAI_YUAN Dai and Yuan FLETCHER Fletcher (Conjugate descent) FLETCHER_REEVES Fletcher and Reeves HAGER_ZHANG Hager and Zhang HESTENES_STIEFEL Hestenes and Stiefel LIU_STOREY Liu and Storey POLAK_RIBIERE Polak and Ribiere ZERO Steepest descent

  EPS_ERROR
  	EPS_ERROR {Real}
  	Target value of the MAX norm of the error  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one real.
  	Default value: 1.00000000E-005

  EPS_ERROR_EARLY
  	EPS_ERROR_EARLY {Real}
  	Target value of the MAX norm of the error for truncated SCF (e.g. Langevin-corrected MD). Negative values mean that this keyword is not used.  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one real.
  	Default value: -1.00000000E+000

  ETA
  	ETA {Real}
  	Must be between 0.0 and 0.25. Rho value below which the optimization of the model function is not accepted and the optimization is restarted from the same point but decreased trust radius. Rho is the ratio of the actual over predicted change in the objective function  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one real.
  	Default value: 2.50000000E-001

  INITIAL_TRUST_RADIUS
  	INITIAL_TRUST_RADIUS {Real}
  	Initial trust radius  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one real.
  	Default value: 1.00000000E-001

  MAX_ITER
  	MAX_ITER {Integer}
  	Maximum number of iterations  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one integer.
  	Default value: 20

  MAX_ITER_EARLY
  	MAX_ITER_EARLY {Integer}
  	Maximum number of iterations for truncated SCF (e.g. Langevin-corrected MD). Negative values mean that this keyword is not used.  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one integer.
  	Default value: -1

  MAX_ITER_OUTER_LOOP
  	MAX_ITER_OUTER_LOOP {Integer}
  	Maximum number of iterations in the outer loop. Use the outer loop to update the preconditioner and reset the conjugator. This can speed up convergence significantly.  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one integer.
  	Default value: 0

  MAX_TRUST_RADIUS
  	MAX_TRUST_RADIUS {Real}
  	Maximum allowed trust radius  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one real.
  	Default value: 2.00000000E+000

  MODEL_GRAD_NORM_RATIO
  	MODEL_GRAD_NORM_RATIO {Real}
  	Stop the fixed-trust-radius (inner) loop optimization once the ratio of the current norm of the model gradient over the initial norm drops below this threshold  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one real.
  	Default value: 1.00000000E-002

  PRECONDITIONER
  	PRECONDITIONER {Keyword}
  	Select a preconditioner for the conjugate gradient optimization  [Edit on GitHub]
  	This keyword cannot be repeated and it expects precisely one keyword.
  	Default value: DEFAULT
  	List of valid keywords: DEFAULT Same as DOMAIN preconditioner DOMAIN Invert preconditioner domain-by-domain. The main component of the linear scaling algorithm FULL Solve linear equations step=-H.grad on the entire space NONE Do not use preconditioner