ALMO_OPTIMIZER_TRUSTR
Controls the trust-region optimization of block-diagonal ALMOs. See XALMO_OPTIMIZER_TRUSTR section for brief explanations. [Edit on GitHub]
Keywords
Keyword descriptions
- ALGORITHM: enum = CAUCHY
Usage: ALGORITHM CG
Valid values:
CGSteihaug’s iterative CG algorithm that does not invert model HessianCAUCHYCompute simple Cauchy pointDOGLEGDogleg optimizer
Selects an algorithm to solve the fixed-radius subproblem [Edit on GitHub]
- CONJUGATOR: enum = HAGER_ZHANG
Usage: CONJUGATOR POLAK_RIBIERE
Valid values:
ZEROSteepest descentPOLAK_RIBIEREPolak and RibiereFLETCHER_REEVESFletcher and ReevesHESTENES_STIEFELHestenes and StiefelFLETCHERFletcher (Conjugate descent)LIU_STOREYLiu and StoreyDAI_YUANDai and YuanHAGER_ZHANGHager 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]
- ETA: real = 2.50000000E-001
Usage: ETA 0.1
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]
- INITIAL_TRUST_RADIUS: real = 1.00000000E-001
Usage: INITIAL_TRUST_RADIUS 0.1
Initial trust radius [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]
- MAX_TRUST_RADIUS: real = 2.00000000E+000
Usage: MAX_TRUST_RADIUS 1.0
Maximum allowed trust radius [Edit on GitHub]
- MODEL_GRAD_NORM_RATIO: real = 1.00000000E-002
Usage: MODEL_GRAD_NORM_RATIO 1.E-2
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]
- PRECONDITIONER: enum = DEFAULT
Usage: PRECONDITIONER DOMAIN
Valid values:
NONEDo not use preconditionerDEFAULTSame as DOMAIN preconditionerDOMAINInvert preconditioner domain-by-domain. The main component of the linear scaling algorithmFULLSolve linear equations step=-H.grad on the entire space
Select a preconditioner for the conjugate gradient optimization [Edit on GitHub]