Optimization function using Gurobi solver (original implementation)

Solves the integer/linear programming problem for computing the minimum test statistic in stratified randomized experiments using the Gurobi solver.

Usage

Gurobi_sol_com(
  Z,
  block,
  weight,
  coeflists,
  p,
  ms_list,
  exact = TRUE,
  block.sum = NULL
)

Arguments

Z

An n-dimensional treatment assignment vector.

block

An n-dimensional vector specifying block of each unit.

weight

A B-dimensional vector of block weights.

coeflists

A list of H elements, each containing B matrices with block-specific test statistics for different values of k.

p

Upper bound on the number of units with effects greater than c.

ms_list

A list containing mu (means) and sigma (standard deviations) for each test statistic.

exact

Logical; if TRUE, solve as integer linear program (ILP). If FALSE, solve as linear program (LP relaxation).

block.sum

Optional pre-computed block summary from summary_block().

Value

A list with:

sol

Solution vector

obj

Optimal objective value

Details

This is the original Gurobi-based implementation. For users without a Gurobi license, HiGHS_sol_com provides equivalent functionality using the open-source HiGHS solver.

See also

HiGHS_sol_com for the HiGHS implementation, solve_optimization for the unified wrapper

Examples

if (FALSE) { # \dontrun{
# This is typically called internally by pval_comb_block()
result <- Gurobi_sol_com(Z, block, weight, coeflists, p, ms_list, exact = TRUE)
} # }