This function solves a very simple optimization problem needed to create fixed-X and
Gaussian SDP knockoffs on the full the covariance matrix. This may be significantly
less powerful than create.solve_sdp.
create.solve_equi(Sigma)The solution \(s\) to the optimization problem defined above.
positive-definite p-by-p covariance matrix.
Computes the closed-form solution to the semidefinite programming problem: $$ \mathrm{maximize} \; s \quad \mathrm{subject} \; \mathrm{to:} \; 0 \leq s \leq 1, \; 2\Sigma - sI \geq 0 $$ used to generate equi-correlated knockoffs.
The closed form-solution to this problem is \(s = 2\lambda_{\mathrm{min}}(\Sigma) \land 1\).
Other optimization:
create.solve_asdp(),
create.solve_sdp()