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backend/new-overpass #52

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kscheidecker merged 32 commits from backend/new-overpass into main 2025-01-23 15:34:23 +00:00
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backend/src/utils/optimizer.py
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@@ -41,7 +41,7 @@ class Optimizer:
resx (list[float]): List of edge weights.
Returns:
tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
tuple[list[int], list[int]]: A tuple containing a new row for A and new value for ub.
"""
for i, elem in enumerate(resx):
@@ -165,19 +165,21 @@ class Optimizer:
def init_ub_dist(self, landmarks: list[Landmark], max_time: int):
def init_ub_time(self, landmarks: list[Landmark], max_time: int):
"""
Initialize the objective function coefficients and inequality constraints for the optimization problem.
Initialize the objective function coefficients and inequality constraints.
This function computes the distances between all landmarks and stores their attractiveness to maximize sightseeing.
The goal is to maximize the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
This function computes the distances between all landmarks and stores
their attractiveness to maximize sightseeing. The goal is to maximize
the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
Args:
landmarks (list[Landmark]): List of landmarks.
max_time (int): Maximum time of visit allowed.
Returns:
tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality
constraint coefficients, and the right-hand side of the inequality constraint.
"""
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
@@ -191,7 +193,7 @@ class Optimizer:
for j, spot2 in enumerate(landmarks) :
t = get_time(spot1.location, spot2.location) + spot1.duration
dist_table[j] = t
closest = sorted(dist_table)[:25]
closest = sorted(dist_table)[:15]
for i, dist in enumerate(dist_table) :
if dist not in closest :
dist_table[i] = 32700
@@ -452,7 +454,7 @@ class Optimizer:
L = len(landmarks)
# SET CONSTRAINTS FOR INEQUALITY
c, A_ub, b_ub = self.init_ub_dist(landmarks, max_time) # Add the distances from each landmark to the other
c, A_ub, b_ub = self.init_ub_time(landmarks, max_time) # Add the distances from each landmark to the other
A, b = self.respect_number(L, max_landmarks) # Respect max number of visits (no more possible stops than landmarks).
A_ub = np.vstack((A_ub, A), dtype=np.int16)
b_ub += b