Description Usage Arguments Value Examples
Provides the default tuning parameters for gforce.FORCE
.
1 |
d |
dimension of random vector or number of datapoints. |
An object with following components
adapt_init_mode
a numeric. Indicates which initialization mode to use for gforce.FORCE_adapt
.
alpha
a numeric. Gives the step size for the projected gradient descent updates.
dual_frequency
an integer. Specifies how many gradient updates to perform between searches for a dual certificate.
duality_gap
a numeric. If the duality gap can be shown to be less than duality_gap
, the FORCE algorithm terminates.
early_stop_mode
a numeric. early_stop_mode == 1
indicates that the algorithm should use an early stopping rule.
early_stop_lag
an integer. This indicates the number of iterations without sufficient improvement in objective value before early stopping.
early_stop_eps
a numeric. Threshold for objective value improvement used to determine early stopping.
eps_obj
a numeric. Specifies the precision required of the optimal solution to the eigenvalue maximization problem.
finish_pgd
an integer. If finish_pgd
is 1, then other stopping criteria are ignored and FORCE performs max_iter
gradient updates.
initial_mixing
a numeric between 0 and 1. Specifies how to construct the initial strictly feasible solution to the SDP relaxation.
kmeans_iter
an integer. The number of times to run a K-means solver during each search for an optimal clustering and dual certificate.
max_iter
an integer. The maximum number of gradient updates to perform.
primal_only
an integer. primal_only == 1
indicates that the algorithm should not search for a dual certificate.
restarts
a vector of integers. This specifies the iterations at which to take the projection of the current iterate and restart the algorithm with that as the initial solution.
verbose
an integer. Specifies the level of verbosity requested from gforce.FORCE.
1 | opts <- gforce.defaults(20)
|
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