XALMO_OPTIMIZER_PCG

Controls the PCG optimization of extended ALMOs. [Edit on GitHub]

Subsections

• XALMO_NEWTON_PCG_SOLVER

Keywords

• CONJUGATOR

• EPS_ERROR

• EPS_ERROR_EARLY

• LIN_SEARCH_EPS_ERROR

• LIN_SEARCH_STEP_SIZE_GUESS

• MAX_ITER

• MAX_ITER_EARLY

• MAX_ITER_OUTER_LOOP

• PRECONDITIONER

• PRECOND_FILTER_THRESHOLD

Keyword descriptions

CONJUGATOR: enum = HAGER_ZHANG

Usage: CONJUGATOR POLAK_RIBIERE

Valid values:

• ZERO Steepest descent

• POLAK_RIBIERE Polak and Ribiere

• FLETCHER_REEVES Fletcher and Reeves

• HESTENES_STIEFEL Hestenes and Stiefel

• FLETCHER Fletcher (Conjugate descent)

• LIU_STOREY Liu and Storey

• DAI_YUAN Dai and Yuan

• HAGER_ZHANG Hager and Zhang

Various methods to compute step directions in the PCG optimization [Edit on GitHub]

EPS_ERROR: real = 1.00000000E-005

Usage: EPS_ERROR 1.E-6

Target value of the MAX norm of the error [Edit on GitHub]

EPS_ERROR_EARLY: real = -1.00000000E+000

Usage: EPS_ERROR_EARLY 1.E-2

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]

LIN_SEARCH_EPS_ERROR: real = 1.00000000E-003

Usage: LIN_SEARCH_EPS_ERROR 1.E-2

Target value of the gradient norm during the linear search [Edit on GitHub]

LIN_SEARCH_STEP_SIZE_GUESS: real = 1.00000000E+000

Usage: LIN_SEARCH_STEP_SIZE_GUESS 0.1

The size of the first step in the linear search [Edit on GitHub]

MAX_ITER: integer = 20

Usage: MAX_ITER 100

Maximum number of iterations [Edit on GitHub]

MAX_ITER_EARLY: integer = -1

Usage: MAX_ITER_EARLY 5

Maximum number of iterations for truncated SCF (e.g. Langevin-corrected MD). Negative values mean that this keyword is not used. [Edit on GitHub]

MAX_ITER_OUTER_LOOP: integer = 0

Usage: MAX_ITER 10

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]

PRECONDITIONER: enum = DEFAULT

Usage: PRECONDITIONER DOMAIN

Valid values:

• NONE Do not use preconditioner

• 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

Select a preconditioner for the conjugate gradient optimization [Edit on GitHub]

PRECOND_FILTER_THRESHOLD: real = -1.00000000E+000

Usage: PRECOND_FILTER_THRESHOLD 0.1

Select eigenvalues of the preconditioner that are smaller than the threshold and project out the corresponding eigenvectors from the gradient. No matter how large the threshold is the maximum number of projected eienvectors for a fragment equals to the number of occupied orbitals of fragment’s neighbors. [Edit on GitHub]