solvr 0.2.0-beta.2

Advanced computing library for real-world problem solving - optimization, differential equations, interpolation, statistics, and more
Documentation 
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//! WebGPU implementation of mixed-integer linear programming.

use crate::optimize::linprog::impl_generic::{
    MilpOptionsInternal, TensorLinearConstraints, milp_impl,
};
use crate::optimize::linprog::{
    LinProgTensorConstraints, MilpAlgorithms, MilpOptions, MilpTensorResult,
};
use numr::error::Result;
use numr::runtime::wgpu::{WgpuClient, WgpuRuntime};
use numr::tensor::Tensor;

impl MilpAlgorithms<WgpuRuntime> for WgpuClient {
    fn milp(
        &self,
        c: &Tensor<WgpuRuntime>,
        constraints: &LinProgTensorConstraints<WgpuRuntime>,
        integrality: &Tensor<WgpuRuntime>,
        options: &MilpOptions,
    ) -> Result<MilpTensorResult<WgpuRuntime>> {
        // Convert constraints to impl_generic format
        let internal_constraints = TensorLinearConstraints {
            a_ub: constraints.a_ub.clone(),
            b_ub: constraints.b_ub.clone(),
            a_eq: constraints.a_eq.clone(),
            b_eq: constraints.b_eq.clone(),
            lower_bounds: constraints.lower_bounds.clone(),
            upper_bounds: constraints.upper_bounds.clone(),
        };

        let internal_options = MilpOptionsInternal {
            max_nodes: options.max_nodes,
            int_tol: options.int_tol,
            gap_tol: options.gap_tol,
            lp_options: options.lp_options.clone(),
        };

        let result = milp_impl(
            self,
            c,
            &internal_constraints,
            integrality,
            &internal_options,
        )
        .map_err(|e| numr::error::Error::backend_limitation("wgpu", "milp", e.to_string()))?;

        Ok(MilpTensorResult {
            x: result.x,
            fun: result.fun,
            success: result.success,
            nodes: result.nodes,
            gap: result.gap,
            message: result.message,
        })
    }
}