Description Usage Arguments Value References Examples
Given a proposed integer solution to the adaptive K-means SDP relaxation, this function attempts to construct a solution to the dual problem with matching objective value.
1 | gforce.certify_adapt(sol, D, eps1 = 10^-7)
|
sol |
vector of length d. This contains the assignment of variables or points to clusters. |
D |
d x d matrix. |
eps1 |
a scalar. It controls the infeasibility tolerance for the dual solution to allow for numerical imprecision. |
An object with the following components:
Y_a
a d dimensional numeric vector. The value of the variable Y_a
in the dual solution found.
feasible
an integer. 1 signifies that sol
is optimal, 0 otherwise.
C. Eisenach and H. Liu. Efficient, Certifiably Optimal High-Dimensional Clustering. arXiv:1806.00530, 2018.
1 2 3 4 5 6 7 8 | K <- 5
n <- 50
d <- 50
dat <- gforce.generator(K,d,n,3,graph='scalefree')
sig_hat <- (1/n)*t(dat$X)%*%dat$X
gam_hat <- gforce.Gamma(dat$X)
D <- diag(gam_hat) - sig_hat
dual_cert <- gforce.certify_adapt(dat$group_assignments,D)
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