From 44f65cf9b5f25911ceb4b98f0cf269f8c4c4958e Mon Sep 17 00:00:00 2001
From: Jared Deckard <jared.deckard@gmail.com>
Date: Fri, 2 Aug 2019 08:05:59 -0500
Subject: [PATCH] Allow optimizing per-move energy changes

---
 README.md           | 7 +++++++
 simanneal/anneal.py | 9 ++++++---
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 975d6a0..ed8207d 100644
--- a/README.md
+++ b/README.md
@@ -135,6 +135,13 @@ them into the constructor like so
 
 The last line (calling init on the super class) is critical. 
 
+## Optimizations
+
+For some problems the `energy` function is prohibitively expensive to calculate
+after every move. It is often possible to compute the change in energy that a
+move causes much more efficiently. A delta value can be returned from `move` to
+update the energy value without calling `energy` multiple times.
+
 ## Implementation Details
 
 The simulated annealing algorithm requires that we track state (current, previous, best) and thus means we need to copy the `self.state` frequently.
diff --git a/simanneal/anneal.py b/simanneal/anneal.py
index ebc5286..6e563d0 100644
--- a/simanneal/anneal.py
+++ b/simanneal/anneal.py
@@ -193,9 +193,12 @@ def anneal(self):
         while step < self.steps and not self.user_exit:
             step += 1
             T = self.Tmax * math.exp(Tfactor * step / self.steps)
-            self.move()
-            E = self.energy()
-            dE = E - prevEnergy
+            dE = self.move()
+            if dE is None:
+                E = self.energy()
+                dE = E - prevEnergy
+            else:
+                E += dE
             trials += 1
             if dE > 0.0 and math.exp(-dE / T) < random.random():
                 # Restore previous state