| import numpy as np |
| import argparse |
| import pickle |
| import random |
| import time |
| import os |
|
|
|
|
| def get_best_solution(n, m, k, site, value, constraint, constraint_type, coefficient, time_limit, obj_type): |
| ''' |
| Function Description: |
| Solve the problem using an optimization solver based on the provided problem instance. |
| |
| Parameters: |
| - n: Number of decision variables in the problem instance. |
| - m: Number of constraints in the problem instance. |
| - k: k[i] represents the number of decision variables in the i-th constraint. |
| - site: site[i][j] represents which decision variable the j-th decision variable of the i-th constraint corresponds to. |
| - value: value[i][j] represents the coefficient of the j-th decision variable in the i-th constraint. |
| - constraint: constraint[i] represents the right-hand side value of the i-th constraint. |
| - constraint_type: constraint_type[i] represents the type of the i-th constraint, where 1 represents <=, 2 represents >=, and 3 represents =.. |
| - coefficient: coefficient[i] represents the coefficient of the i-th decision variable in the objective function. |
| - time_limit: Maximum solving time. |
| - obj_type: Whether the problem is a maximization problem or a minimization problem. |
| |
| Return: |
| The optimal solution of the problem, represented as a list of values for each decision variable in the optimal solution. |
| ''' |
|
|
| raise NotImplementedError('get_best_solution method should be implemented') |
|
|
|
|
| def optimize( |
| time : int, |
| number : int, |
| ): |
| ''' |
| Function Description: |
| Based on the specified parameter design, invoke the designated algorithm and solver to optimize the optimization problem in data.pickle in the current directory. |
| |
| Parameters: |
| - number: Integer value indicating the number of instances to generate. |
| - suboptimal: Integer value indicating the number of suboptimal solutions to generate. |
| |
| Return: |
| The optimal solution is generated and packaged as data.pickle. The function does not have a return value. |
| ''' |
|
|
|
|
| for num in range(number): |
| |
| if(os.path.exists('./example/data' + str(num) + '.pickle') == False): |
| print("No input file!") |
| return |
| with open('./example/data' + str(num) + '.pickle', "rb") as f: |
| data = pickle.load(f) |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| n = data[1] |
| m = data[2] |
| k = data[3] |
| site = data[4] |
| value = data[5] |
| constraint = data[6] |
| constraint_type = data[7] |
| coefficient = data[8] |
| |
| |
| obj_type = data[0] |
| optimal_solution = get_best_solution(n, m, k, site, value, constraint, constraint_type, coefficient, time, obj_type) |
| |
|
|
| variable_features = [] |
| constraint_features = [] |
| edge_indices = [[], []] |
| edge_features = [] |
|
|
| for i in range(n): |
| now_variable_features = [] |
| now_variable_features.append(coefficient[i]) |
| now_variable_features.append(0) |
| now_variable_features.append(1) |
| now_variable_features.append(1) |
| now_variable_features.append(random.random()) |
| variable_features.append(now_variable_features) |
| |
| for i in range(m): |
| now_constraint_features = [] |
| now_constraint_features.append(constraint[i]) |
| now_constraint_features.append(constraint_type[i]) |
| now_constraint_features.append(random.random()) |
| constraint_features.append(now_constraint_features) |
| |
| for i in range(m): |
| for j in range(k[i]): |
| edge_indices[0].append(i) |
| edge_indices[1].append(site[i][j]) |
| edge_features.append([value[i][j]]) |
|
|
| with open('./example/sample' + str(num) + '.pickle', 'wb') as f: |
| pickle.dump([variable_features, constraint_features, edge_indices, edge_features, optimal_solution], f) |
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('--time', type = int, default = 10, help = 'Running wall-clock time.') |
| parser.add_argument("--number", type = int, default = 10, help = 'The number of instances.') |
| return parser.parse_args() |
|
|
|
|
|
|
| if __name__ == '__main__': |
| args = parse_args() |
| |
| optimize(**vars(args)) |
