From 14385342ccb4519ca5c5631a6db2fda0c1ef322c Mon Sep 17 00:00:00 2001
From: Helldragon67 <kilian.scheidecker@orange.fr>
Date: Wed, 15 Jan 2025 09:35:25 +0100
Subject: [PATCH] better sym breaking

---
 backend/src/tests/test_main.py |  1 +
 backend/src/utils/optimizer.py | 16 ++++++++--------
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/backend/src/tests/test_main.py b/backend/src/tests/test_main.py
index 1a353f4..92b7be6 100644
--- a/backend/src/tests/test_main.py
+++ b/backend/src/tests/test_main.py
@@ -35,6 +35,7 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
             }
         )
     result = response.json()
+    print(result)
     landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
 
 
diff --git a/backend/src/utils/optimizer.py b/backend/src/utils/optimizer.py
index 7d4d345..77b30de 100644
--- a/backend/src/utils/optimizer.py
+++ b/backend/src/utils/optimizer.py
@@ -35,7 +35,7 @@ class Optimizer:
         """
         Initialize the objective function coefficients and inequality constraints.
         -> Adds 1 row of constraints
-        -> Pre-allocates A_ub for the rest of the computations with 2*L rows
+        -> Pre-allocates A_ub for the rest of the computations with L + (L*L-L)/2 rows
 
         This function computes the distances between all landmarks and stores
         their attractiveness to maximize sightseeing. The goal is to maximize 
@@ -55,8 +55,8 @@ class Optimizer:
         c = np.zeros(L, dtype=np.int16)
 
         # Coefficients of inequality constraints (left-hand side)
-        A_ub = np.zeros((2*L, L*L), dtype=np.int16)
-        b_ub = np.zeros(2*L, dtype=np.int16)
+        A_ub = np.zeros((L + int((L*L-L)/2), L*L), dtype=np.int16)
+        b_ub = np.zeros(L + int((L*L-L)/2), dtype=np.int16)
 
         # Fill in first row
         b_ub[0] = round(max_time*self.overshoot)
@@ -65,9 +65,9 @@ class Optimizer:
             c[i] = -spot1.attractiveness
             for j in range(i+1, L) :
                 if i !=j :
-                    t = get_time(spot1.location, landmarks[j].location) + spot1.duration
-                    A_ub[0, i*L + j] = t
-                    A_ub[0, j*L + i] = t
+                    t = get_time(spot1.location, landmarks[j].location)
+                    A_ub[0, i*L + j] = t  + spot1.duration
+                    A_ub[0, j*L + i] = t + landmarks[j].duration
 
         # Expand 'c' to L*L for every decision variable
         c = np.tile(c, L)     
@@ -120,7 +120,7 @@ class Optimizer:
         Generate constraints to prevent simultaneous travel between two landmarks
         in both directions. Constraint to not have d14 and d41 simultaneously. 
         Does not prevent cyclic paths with more elements
-        -> Adds L rows of constraints (some of which might be zero)
+        -> Adds (L*L-L)/2 rows of constraints (some of which might be zero)
 
         Args:
             L (int): Number of landmarks.
@@ -136,7 +136,7 @@ class Optimizer:
 
         # A = np.zeros((len(up_ind_x[1:]),L*L), dtype=np.int8)
         # Fill-in rows L to 2*L-1
-        for i in range(L) :
+        for i in range(int((L*L-L)/2)) :
             if up_ind_x[i] != up_ind_y[i] :
                 A[L+i, up_ind_x[i]*L + up_ind_y[i]] = 1
                 A[L+i, up_ind_y[i]*L + up_ind_x[i]] = 1