gforce.PECOK: Solve PECOK with FORCE.

In GFORCE: Clustering and Inference Procedures for High-Dimensional Latent Variable Models

Description

Uses the FORCE algorithm to solve the PECOK SDP.

Usage

gforce.PECOK(K, X = NULL, D = NULL, sigma_hat = NULL,
  force_opts = NULL, X0 = NULL, E = NULL, gamma_par = FALSE)

Arguments

K	number of clusters.
X	n x d matrix. Either this or D must be specified.
D	d x d matrix. Either this or X must be specified.
sigma_hat	d x d matrix. If D is specified, this argument should be the estimated covariance matrix. It is not strictly necessary to provide it, but it should be for optimal performance. If X is specified, it will be ignored.
force_opts	tuning parameters. NULL signifies defaults will be used.
X0	initial iterate. NULL signifies that it will be generated randomly from D_Kmeans. If supplied, E must be supplied as well.
E	strictly feasible solutions. NULL signifies that it will be generated randomly. If supplied, X0 must be supplied as well.
gamma_par	logical expression. If gamma_par==TRUE, then if Γ is computed, a multi-threaded method is called, otherwise a single-threaded method is called.

References

C. Eisenach and H. Liu. Efficient, Certifiably Optimal High-Dimensional Clustering. arXiv:1806.00530, 2018.

J. Peng and Y. Wei. Approximating K-means-type Clustering via Semidefinite Programming. SIAM Journal on Optimization, 2007.

F. Bunea, C. Giraud, M. Royer and N. Verzelen. PECOK: a convex optimization approach to variable clustering. arXiv:1606.05100, 2016.

See Also

gforce.defaults

GFORCE documentation built on May 2, 2019, 3:44 a.m.