diff --git a/backend/app/__pycache__/main.cpython-310.pyc b/backend/app/__pycache__/main.cpython-310.pyc
index 21f3cee..cd67059 100644
Binary files a/backend/app/__pycache__/main.cpython-310.pyc and b/backend/app/__pycache__/main.cpython-310.pyc differ
diff --git a/backend/app/main.py b/backend/app/main.py
index 84b3141..d7e153c 100644
--- a/backend/app/main.py
+++ b/backend/app/main.py
@@ -5,8 +5,8 @@ from fastapi import FastAPI
 app = FastAPI()
 
 
-@app.get("/optimize/{max_steps}/{print_to_console}")
-def main(max_steps: int, print_to_console: bool):
+@app.get("/optimize/{max_steps}/{print_details}")
+def main(max_steps: int, print_details: bool):
     
     # CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
     #max_steps = 16
@@ -23,7 +23,7 @@ def main(max_steps: int, print_to_console: bool):
     landmarks.append(landmark("arrivée", -1, (0, 0)))
 
 
-    visiting_order = solve_optimization(landmarks, max_steps, print_to_console)
+    visiting_order = solve_optimization(landmarks, max_steps, print_details)
 
     #return visiting_order
 
diff --git a/backend/app/src/__pycache__/optimizer.cpython-310.pyc b/backend/app/src/__pycache__/optimizer.cpython-310.pyc
index 55a6337..e60b82c 100644
Binary files a/backend/app/src/__pycache__/optimizer.cpython-310.pyc and b/backend/app/src/__pycache__/optimizer.cpython-310.pyc differ
diff --git a/backend/app/src/optimizer.py b/backend/app/src/optimizer.py
index 4c1b631..2681469 100644
--- a/backend/app/src/optimizer.py
+++ b/backend/app/src/optimizer.py
@@ -261,7 +261,7 @@ def path_length(P: list, resx: list) :
     return np.dot(P, resx)
 
 # Main optimization pipeline
-def solve_optimization (landmarks, max_steps, printing_console) :
+def solve_optimization (landmarks, max_steps, printing_details) :
 
     # SET CONSTRAINTS FOR INEQUALITY
     c, A_ub, b_ub = init_ub_dist(landmarks, max_steps)              # Add the distances from each landmark to the other
@@ -283,15 +283,7 @@ def solve_optimization (landmarks, max_steps, printing_console) :
     # Solve linear programming problem
 
     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)
-    circle = has_circle(res.x)
-    i = 0
-
-    # Break the circular symmetry if needed
-    while len(circle) != 0 :
-        A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
-        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)
-        circle = has_circle(res.x)
-        i += 1
+    
 
 
     # Raise error if no solution is found
@@ -305,16 +297,29 @@ def solve_optimization (landmarks, max_steps, printing_console) :
         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)
 
         if not res.success :
-            raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
+            s = "No solution could be found, even when increasing max_steps using the heuristic"
+            return s
+            #raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
     
+    # If there is a solution, we're good to go, just check for
+    else :
+        circle = has_circle(res.x)
+        i = 0
 
-    if printing_console is True :
-        if i != 0 :
-            print(f"Neded to recompute paths {i} times because of unconnected loops...")
+        # Break the circular symmetry if needed
+        while len(circle) != 0 :
+            A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
+            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)
+            circle = has_circle(res.x)
+            i += 1
+
+        if printing_details is True :
+            if i != 0 :
+                print(f"Neded to recompute paths {i} times because of unconnected loops...")
             X = print_res(res, landmarks, P)
             return X
-    else :
-        return untangle(res.x)
+        else :
+            return untangle(res.x)