always-cautious/ortools-clone
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

polish python examples

Browse Source
This commit is contained in:
Laurent Perron
2023-02-17 13:59:43 +01:00
parent 3ff130df97
commit ebb02b4f8e
2 changed files with 21 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
examples/python/bus_driver_scheduling_sat.py
View File

@@ -37,8 +37,8 @@ _PARAMS = flags.DEFINE_string(
'params',
'num_search_workers:16,log_search_progress:true,max_time_in_seconds:30',
'Sat solver parameters.')
_INSTANCE = flags.DEFINE_integer('instance', 0, 'Instance to select (0, 1, 2, 3).',
0, 3)
_INSTANCE = flags.DEFINE_integer('instance', 0,
'Instance to select (0, 1, 2, 3).', 0, 3)
SAMPLE_SHIFTS_TINY = [
#

43
examples/python/spread_robots_sat.py
View File

@@ -13,6 +13,7 @@
# limitations under the License.
"""Maximize the minimum of pairwise distances between n robots in a square space."""
import math
from typing import Sequence
from absl import app
from absl import flags
@@ -42,29 +43,22 @@ def spread_robots(num_robots: int, room_size: int, params: str):
# The specification of the problem is to maximize the minimum euclidian
# distance between any two robots. Unfortunately, the euclidian distance
# uses the square root operation which is not defined on integer variables.
# To work around, we will create a distance variable, then make sure that
# its square value is less than the square of the euclidian distance between
# any two robots.
# To work around, we will create a min_square_distance variable, then we make
# sure that its value is less than the square of the euclidian distance
# between any two robots.
#
# This encoding has a low precision. To improve the precision, we will scale
# the domain of the min distance variable by a constant factor, then multiply
# the square of the euclidian distance between two robots by the square of
# this constant factor.
# the domain of the min_square_distance variable by a constant factor, then
# multiply the square of the euclidian distance between two robots by the same
# factor.
#
# we create a scaled_min_distance variable with a domain of
# [0..scaling * max euclidian distance] such that
# we create a scaled_min_square_distance variable with a domain of
# [0..scaling * max euclidian distance**2] such that
# forall i:
# scaled_min_distance**2 <= scaling_sq * (x_diff_sq[i] + y_diff_sq[i])
scaling = 100
squaling_sq = scaling**2
scaled_room_size = room_size * scaling
# Max scaled distance is actually scaling * room_size * sqrt(2).
scaled_min_distance = model.NewIntVar(0, 2 * scaled_room_size,
'scaled_min_distance')
scaled_min_square_distance = model.NewIntVar(0, 2 * scaled_room_size**2,
# scaled_min_square_distance <= scaling * (x_diff_sq[i] + y_diff_sq[i])
scaling = 1000
scaled_min_square_distance = model.NewIntVar(0, 2 * scaling * room_size**2,
'scaled_min_square_distance')
model.AddMultiplicationEquality(scaled_min_square_distance,
scaled_min_distance, scaled_min_distance)
# Build intermediate variables and get the list of squared distances on
# each dimension.
@@ -82,10 +76,11 @@ def spread_robots(num_robots: int, room_size: int, params: str):
model.AddMultiplicationEquality(x_diff_sq, x_diff, x_diff)
model.AddMultiplicationEquality(y_diff_sq, y_diff, y_diff)
# We just need to be <= to the real scaled distance as we are
# maximizing the min distance.
model.Add(scaled_min_square_distance <= x_diff_sq * squaling_sq +
y_diff_sq * squaling_sq)
# We just need to be <= to the scaled square distance as we are
# maximizing the min distance, which is equivalent as maximizing the min
# square distance.
model.Add(scaled_min_square_distance <= scaling *
(x_diff_sq + y_diff_sq))
# Naive symmetry breaking.
for i in range(1, num_robots):
@@ -93,7 +88,7 @@ def spread_robots(num_robots: int, room_size: int, params: str):
model.Add(y[0] <= y[i])
# Objective
model.Maximize(scaled_min_distance)
model.Maximize(scaled_min_square_distance)
# Creates a solver and solves the model.
solver = cp_model.CpSolver()
@@ -105,7 +100,7 @@ def spread_robots(num_robots: int, room_size: int, params: str):
# Prints the solution.
if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:
print(f'Spread {num_robots} with a min pairwise distance of'
f' {solver.ObjectiveValue() / scaling}')
f' {math.sqrt(solver.ObjectiveValue() / scaling)}')
for i in range(num_robots):
print(f'robot {i}: x={solver.Value(x[i])} y={solver.Value(y[i])}')
else: