Solves the inner-loop QP for SCM: given a fixed diagonal metric matrix V, finds donor weights W on the simplex minimising the V-weighted covariate loss. The returned weights are a KKT-verified exact optimum whenever the active-set solver converges, with accelerated projected gradient as a fallback.
Arguments
- X0
Covariate matrix for control units (k x N_co)
- X1
Covariate vector for the treated unit (k x 1)
- V_diag
Diagonal of the metric matrix V (k x 1, non-negative, need not sum to 1)
- wolfe
If
TRUE, solve with the Wolfe min-norm-point method, which returns a Caratheodory-sparse optimum (at most k+1 donors carry weight) instead of one arbitrary point of a degenerate optimal face.FALSE(default) uses the warm-started active-set solver.