Optimize dense solvers

Author: WrathfulSpatula

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pyqrackising"
-version = "9.16.0"
+version = "9.16.1"
 requires-python = ">=3.8"
 description = "Fast MAXCUT, TSP, and sampling heuristics from near-ideal transverse field Ising model (TFIM)"
 readme = {file = "README.txt", content-type = "text/markdown"}

pyqrackising/maxcut_tfim.py

@@ -37,15 +37,13 @@
 def update_repulsion_choice(G_m, weights, n, used, node, repulsion_base):
     # Select node
     used[node] = True
+    weights[node] = 0.0
 
     if abs(1.0 - repulsion_base) <= epsilon:
         return
 
     # Repulsion: penalize neighbors
-    for nbr in range(n):
-        if used[nbr]:
-            continue
-        weights[nbr] *= repulsion_base ** (-G_m[node, nbr])
+    weights *= repulsion_base ** (-G_m[node])
 
 
 # Written by Elara (OpenAI custom GPT) and improved by Dan Strano

setup.py

@@ -7,7 +7,7 @@
 
 setup(
     name="pyqrackising",
-    version="9.16.0",
+    version="9.16.1",
     author="Dan Strano",
     author_email="stranoj@gmail.com",
     description="Fast MAXCUT, TSP, and sampling heuristics from near-ideal transverse field Ising model (TFIM)",