permafixed the optimizer ???

backend/src/amenities/nature.am

@@ -8,5 +8,5 @@ geological
 'tourism'='alpine_hut'
 'tourism'='viewpoint'
 'tourism'='zoo'
-#'tourism'='artwork'
+'tourism'='artwork'
 'waterway'='waterfall'

backend/src/optimizer_v4.py

@@ -31,7 +31,7 @@ def print_res(L: List[Landmark], L_tot):
 
 
 # Prevent the use of a particular solution
-def prevent_config(resx, A_ub, b_ub):
+def prevent_config(resx):
     
     for i, elem in enumerate(resx):
         resx[i] = round(elem)
@@ -52,13 +52,10 @@ def prevent_config(resx, A_ub, b_ub):
         if i in vertices_visited :
             h[i*L:i*L+L] = ones
 
-    A_ub = np.vstack((A_ub, h))
+    return h, [len(vertices_visited)-1]
-    b_ub.append(len(vertices_visited)-1)
-
-    return A_ub, b_ub
 
 
-def prevent_circle(circle_vertices: list, L: int, A_eq: list, b_eq: list) :
+def prevent_circle(circle_vertices: list, L: int) :
 
     l1 = [0]*L*L
     l2 = [0]*L*L
@@ -74,12 +71,7 @@ def prevent_circle(circle_vertices: list, L: int, A_eq: list, b_eq: list) :
     l1[g*L + s] = 1
     l2[s*L + g] = 1
 
-    A_eq = np.vstack((A_eq, l1))
+    return np.vstack((l1, l2)), [0, 0]
-    b_eq.append(0)
-    A_eq = np.vstack((A_eq, l2))
-    b_eq.append(0)
-
-    return A_eq, b_eq
 
 # Prevent the possibility of a given solution bit
 def break_circle(circle_vertices: list, L: int, A_ub: list, b_ub: list):
@@ -340,14 +332,16 @@ def init_ub_dist(landmarks: List[Landmark], max_steps: int):
 
 
 # Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
-def respect_number(L: int, A_ub, b_ub, max_landmarks):
+def respect_number(L: int, max_landmarks: int):
 
     ones = [1]*L
     zeros = [0]*L
-    for i in range(L) :
+    A = ones + zeros*(L-1)
-        h = zeros*i + ones + zeros*(L-1-i)
+    b = [1]
-        A_ub = np.vstack((A_ub, h))
+    for i in range(L-1) :
-        b_ub.append(1)
+        h_new = zeros*i + ones + zeros*(L-1-i)
+        A = np.vstack((A, h_new))
+        b.append(1)
 
     if max_landmarks is None :
         # Read the parameters from the file
@@ -355,29 +349,35 @@ def respect_number(L: int, A_ub, b_ub, max_landmarks):
             parameters = json.loads(f.read())
             max_landmarks = parameters['max landmarks']
 
-    A_ub = np.vstack((A_ub, ones*L))
+    A = np.vstack((A, ones*L))
-    b_ub.append(max_landmarks+1)
+    b.append(max_landmarks+1)
 
-    return A_ub, b_ub
+    return A, b
 
 
-# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
+# Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
-def break_sym(L, A_ub, b_ub):
+def break_sym(L):
+
     upper_ind = np.triu_indices(L,0,L)
 
     up_ind_x = upper_ind[0]
     up_ind_y = upper_ind[1]
 
-    for i, _ in enumerate(up_ind_x) :
+    A = [0]*L*L         # useless row to prevent overhead ? better solution welcomed
+    # A[up_ind_x[0]*L + up_ind_y[0]] = 1
+    # A[up_ind_y[0]*L + up_ind_x[0]] = 1
+    b = [1]
+
+    for i, _ in enumerate(up_ind_x[1:]) :
         l = [0]*L*L
         if up_ind_x[i] != up_ind_y[i] :
             l[up_ind_x[i]*L + up_ind_y[i]] = 1
             l[up_ind_y[i]*L + up_ind_x[i]] = 1
 
-            A_ub = np.vstack((A_ub,l))
+            A = np.vstack((A,l))
-            b_ub.append(1)
+            b.append(1)
 
-    return A_ub, b_ub
+    return A, b
 
 
 # Constraint to not stay in position. Removes d11, d22, d33, etc.
@@ -395,22 +395,24 @@ def init_eq_not_stay(L: int):
 
 
 # Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
+def respect_user_mustsee(landmarks: List[Landmark]) :
     L = len(landmarks)
 
-    for i, elem in enumerate(landmarks) :
+    A = [0]*L*L
+    b = [0]
+    for i, elem in enumerate(landmarks[1:]) :
         if elem.must_do is True and elem.name not in ['finish', 'start']:
             l = [0]*L*L
             l[i*L:i*L+L] = [1]*L        # set mandatory departures from landmarks tagged as 'must_do'
 
-            A_eq = np.vstack((A_eq,l))
+            A = np.vstack((A,l))
-            b_eq.append(1)
+            b.append(1)
 
-    return A_eq, b_eq
+    return A, b
 
 
 # Constraint to ensure start at start and finish at goal
-def respect_start_finish(L: int, A_eq: list, b_eq: list):
+def respect_start_finish(L: int):
     l_start = [1]*L + [0]*L*(L-1)   # sets departures only for start (horizontal ones)
     l_start[L-1] = 0                # prevents the jump from start to finish
     l_goal = [0]*L*L                # sets arrivals only for finish (vertical ones)
@@ -421,32 +423,33 @@ def respect_start_finish(L: int, A_eq: list, b_eq: list):
             l_goal[k*L+L-1] = 1 
             
 
-    A_eq = np.vstack((A_eq,l_start))
+    A = np.vstack((l_start, l_goal))
-    A_eq = np.vstack((A_eq,l_goal))
+    b = [1, 1]
-    A_eq = np.vstack((A_eq,l_L))
+    A = np.vstack((A,l_L))
-    b_eq.append(1)
+    b.append(0)
-    b_eq.append(1)
-    b_eq.append(0)
 
-    return A_eq, b_eq
+    return A, b
 
 
 # Constraint to tie the problem together. Necessary but not sufficient to avoid circles
-def respect_order(N: int, A_eq, b_eq): 
+def respect_order(L: int): 
-    for i in range(N-1) :           # Prevent stacked ones
+
-        if i == 0 or i == N-1:      # Don't touch start or finish
+    A = [0]*L*L                     # useless row to reduce overhead ? better solution is welcome
+    b = [0]
+    for i in range(L-1) :           # Prevent stacked ones
+        if i == 0 or i == L-1:      # Don't touch start or finish
             continue
         else : 
-            l = [0]*N
+            l = [0]*L
             l[i] = -1
-            l = l*N
+            l = l*L
-            for j in range(N) :
+            for j in range(L) :
-                l[i*N + j] = 1
+                l[i*L + j] = 1
 
-            A_eq = np.vstack((A_eq,l))
+            A = np.vstack((A,l))
-            b_eq.append(0)
+            b.append(0)
 
-    return A_eq, b_eq
+    return A, b
 
 
 # Computes the time to reach from each landmark to the next
@@ -514,15 +517,26 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
     L = len(landmarks)
 
     # SET CONSTRAINTS FOR INEQUALITY
-    c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
+    c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)          # Add the distances from each landmark to the other
-    A_ub, b_ub = respect_number(L, A_ub, b_ub, max_landmarks)                      # Respect max number of visits (no more possible stops than landmarks). 
+    A, b = respect_number(L, max_landmarks)                     # Respect max number of visits (no more possible stops than landmarks). 
-    A_ub, b_ub = break_sym(L, A_ub, b_ub)                           # break the 'zig-zag' symmetry
+    A_ub = np.vstack((A_ub, A))
+    b_ub += b
+    A, b = break_sym(L)                                         # break the 'zig-zag' symmetry
+    A_ub = np.vstack((A_ub, A))
+    b_ub += b
+
 
     # SET CONSTRAINTS FOR EQUALITY
-    A_eq, b_eq = init_eq_not_stay(L)                                # Force solution not to stay in same place
+    A_eq, b_eq = init_eq_not_stay(L)                            # Force solution not to stay in same place
-    A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq)     # Check if there are user_defined must_see. Also takes care of start/goal
+    A, b = respect_user_mustsee(landmarks)                      # Check if there are user_defined must_see. Also takes care of start/goal
-    A_eq, b_eq = respect_start_finish(L, A_eq, b_eq)                # Force start and finish positions
+    A_eq = np.vstack((A_eq, A))
-    A_eq, b_eq = respect_order(L, A_eq, b_eq)                       # Respect order of visit (only works when max_steps is limiting factor)
+    b_eq += b
+    A, b = respect_start_finish(L)                  # Force start and finish positions
+    A_eq = np.vstack((A_eq, A))
+    b_eq += b
+    A, b = respect_order(L)                         # Respect order of visit (only works when max_steps is limiting factor)
+    A_eq = np.vstack((A_eq, A))
+    b_eq += b
     
     # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
     x_bounds = [(0, 1)]*L*L
@@ -541,10 +555,14 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
         i = 0
         timeout = 80
         while circles is not None and i < timeout:
-            A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
+            A, b = prevent_config(res.x)
+            A_ub = np.vstack((A_ub, A))
+            b_ub += b
             #A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
             for circle in circles :
-                A_ub, b_ub = prevent_circle(circle, len(landmarks), A_ub, b_ub)
+                A, b = prevent_circle(circle, len(landmarks))
+                A_eq = np.vstack((A_eq, A))
+                b_eq += b
             res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
             order, circles = is_connected2(res.x)
             #nodes, edges = is_connected2(res.x)

backend/src/tester.py

@@ -99,8 +99,8 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
     landmarks_short.insert(0, start)
     landmarks_short.append(finish)
 
-    max_walking_time = 120      # minutes
+    max_walking_time = 50      # minutes
-    detour = 30                 # minutes
+    detour = 0                 # minutes
 
     # First stage optimization
     base_tour = solve_optimization(landmarks_short, max_walking_time, True)