From 2d0ff28b1becae4b9fc02f9b403f9de810d68da6 Mon Sep 17 00:00:00 2001
From: Oscar Smith <oscardssmith@gmail.com>
Date: Tue, 24 Mar 2026 02:04:05 -0400
Subject: [PATCH 1/4] simplify objective functions

since the log(A_ij) is constant, there's no need to include it in the linear objectives, which allows for simplifying the loop dramatically (at least when the matrix has no zeros)
---
 ext/SIAJuMP.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ext/SIAJuMP.jl b/ext/SIAJuMP.jl
index a06203b..ff7855f 100644
--- a/ext/SIAJuMP.jl
+++ b/ext/SIAJuMP.jl
@@ -33,7 +33,7 @@ function ScaleInvariantAnalysis.symcover_lmin(A)
     model = JuMP.Model(HiGHS.Optimizer)
     JuMP.set_silent(model)
     @variable(model, α[1:n])
-    @objective(model, Min, sum(α[i] + α[j] - logA[i, j] for i in 1:n, j in 1:n if A[i, j] != 0))
+    @objective(model, Min, sum(α))
     for i in 1:n
         for j in i:n
             if A[i, j] != 0
@@ -74,7 +74,7 @@ function ScaleInvariantAnalysis.cover_lmin(A)
     JuMP.set_silent(model)
     @variable(model, α[1:m])
     @variable(model, β[1:n])
-    @objective(model, Min, sum(α[i] + β[j] - logA[i, j] for i in 1:m, j in 1:n if A[i, j] != 0))
+    @objective(model, Min, sum(α) + sum(β))
     for i in 1:m
         for j in 1:n
             if A[i, j] != 0

From 03cee047beaa5804a6c0944783edd990dccdbb3e Mon Sep 17 00:00:00 2001
From: Oscar Smith <oscardssmith@gmail.com>
Date: Tue, 24 Mar 2026 12:56:03 -0400
Subject: [PATCH 2/4] account for zeros of `A`

---
 ext/SIAJuMP.jl | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/ext/SIAJuMP.jl b/ext/SIAJuMP.jl
index ff7855f..35babf8 100644
--- a/ext/SIAJuMP.jl
+++ b/ext/SIAJuMP.jl
@@ -33,7 +33,10 @@ function ScaleInvariantAnalysis.symcover_lmin(A)
     model = JuMP.Model(HiGHS.Optimizer)
     JuMP.set_silent(model)
     @variable(model, α[1:n])
-    @objective(model, Min, sum(α))
+
+    nonzero_rows = count(!iszero, A, dims=1)
+    nonzero_cols = count(!iszero, A, dims=2)
+    @objective(model, Min, dot(α, nonzero_rows.*nonzero_cols))
     for i in 1:n
         for j in i:n
             if A[i, j] != 0
@@ -74,7 +77,10 @@ function ScaleInvariantAnalysis.cover_lmin(A)
     JuMP.set_silent(model)
     @variable(model, α[1:m])
     @variable(model, β[1:n])
-    @objective(model, Min, sum(α) + sum(β))
+
+    nonzero_rows = count(!iszero, A, dims=1)
+    nonzero_cols = count(!iszero, A, dims=2)
+    @objective(model, Min, dot(α, nonzero_rows) + dot(β, nonzero_cols))
     for i in 1:m
         for j in 1:n
             if A[i, j] != 0

From 446a03e43c64012e0d0183a2ad4ee9f816c50cef Mon Sep 17 00:00:00 2001
From: Oscar Smith <oscardssmith@gmail.com>
Date: Tue, 24 Mar 2026 13:08:20 -0400
Subject: [PATCH 3/4] fix

---
 ext/SIAJuMP.jl | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ext/SIAJuMP.jl b/ext/SIAJuMP.jl
index 35babf8..d192ee3 100644
--- a/ext/SIAJuMP.jl
+++ b/ext/SIAJuMP.jl
@@ -3,6 +3,7 @@ module SIAJuMP
 using JuMP: JuMP, @variable, @objective, @constraint
 using HiGHS: HiGHS
 using ScaleInvariantAnalysis
+using LinearAlgebra: dot
 
 # Reference implementation for a symmetric matrix cover
 function ScaleInvariantAnalysis.symcover_qmin(A)
@@ -34,9 +35,9 @@ function ScaleInvariantAnalysis.symcover_lmin(A)
     JuMP.set_silent(model)
     @variable(model, α[1:n])
 
-    nonzero_rows = count(!iszero, A, dims=1)
+    nonzero_rows = count(!iszero, A, dims=1)'
     nonzero_cols = count(!iszero, A, dims=2)
-    @objective(model, Min, dot(α, nonzero_rows.*nonzero_cols))
+    @objective(model, Min, dot(α, nonzero_rows.+nonzero_cols))
     for i in 1:n
         for j in i:n
             if A[i, j] != 0

From a8cf11ae918bab34e37177d9d24b85b19bb2cb67 Mon Sep 17 00:00:00 2001
From: Oscar Smith <oscardssmith@gmail.com>
Date: Tue, 24 Mar 2026 13:13:55 -0400
Subject: [PATCH 4/4] fix nonsymmetric

---
 ext/SIAJuMP.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ext/SIAJuMP.jl b/ext/SIAJuMP.jl
index d192ee3..1da9871 100644
--- a/ext/SIAJuMP.jl
+++ b/ext/SIAJuMP.jl
@@ -79,9 +79,9 @@ function ScaleInvariantAnalysis.cover_lmin(A)
     @variable(model, α[1:m])
     @variable(model, β[1:n])
 
-    nonzero_rows = count(!iszero, A, dims=1)
+    nonzero_rows = count(!iszero, A, dims=1)'
     nonzero_cols = count(!iszero, A, dims=2)
-    @objective(model, Min, dot(α, nonzero_rows) + dot(β, nonzero_cols))
+    @objective(model, Min, dot(α, nonzero_cols) + dot(β, nonzero_rows))
     for i in 1:m
         for j in 1:n
             if A[i, j] != 0