From 1aedf10c5e2c7feeba478b10998c1392b6d42467 Mon Sep 17 00:00:00 2001 From: Brian Doolittle Date: Tue, 5 Dec 2023 18:10:58 -0500 Subject: [PATCH] supporting softtware note --- src/LocalPolytope/linear_nonclassicality_witnesses.jl | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/LocalPolytope/linear_nonclassicality_witnesses.jl b/src/LocalPolytope/linear_nonclassicality_witnesses.jl index 217c349..b460e4a 100644 --- a/src/LocalPolytope/linear_nonclassicality_witnesses.jl +++ b/src/LocalPolytope/linear_nonclassicality_witnesses.jl @@ -27,6 +27,11 @@ technique the output linear inequality witnesses the nonclassicality of the `tes The optimized facet inequality ``(\\mathbf{G}^\\star, \\beta^\\star)`` is returned as a vector ``(G^\\star_{0,0}, \\dots, G^\\star_{Y,X}, -\\beta^\\star)`` where ``G^\\star_{y,x}`` are the elements of ``\\mathbf{G}^\\star``. +!!! note "Supporting Software" + The linear programming is performed using [HiGHS](https://highs.dev/) + solver via the [`JuMP`](https://jump.dev/JuMP.jl/stable/) + interface. Please refer to the source code for more details. + !!! note "Converting Output into Bell Game" The linear programming software outputs numerical values that have numerical error. Moreover, the linear inequality is scaled such that the classical bound is zero and the `test_behavior` score is one. In order to convert the output