PulP добавляет ограничения слишком медленно

Question

Я прочитал этот пост

PuLP очень медленный при добавлении многих ограничений

и до сих пор не замечают решения.

def choose_lines_to_satisfy_image(lines_list_high_res, image_low_res, lines_to_low_res_matrix, allowed_diff_between_targe_pixel_and_result):
    problem = LpProblem("Solving Lines Discretely", LpMinimize)  # minimize or maximize doesn't matter
    problem += 0  # objective function must be added first. it is zero, because we are only looking for a feasable solution. nothing to optimize.

    number_of_lines = len(lines_list_high_res)
    lines_names_list = []

    for i in range(len(lines_list_high_res)):
        line_name = str(i)
        lines_names_list.append(line_name)

    print("len(lines_names_list)")
    print(len(lines_names_list))
    line_variables = LpVariable.dicts("line", lines_names_list, lowBound=0, upBound=1, cat=LpInteger)
    # print('dicts = ')
    # print(line_variables)#can't do this. python hangs. want it? spit it to a log file.

    num_of_lines, num_of_low_res_pixels = lines_to_low_res_matrix.shape
    if len(lines_list_high_res) != num_of_lines:
        print("oh crap")
        return

    # add constraints
    # each constraint is: |pixel-expected-value - pixel-actual-value| < allowed_diff_between_targe_pixel_and_result
    # which translates to 2 constraints:
    # 1) sum(lines coefficients through pixel) < pixel-expected-value + allowed_diff_between_targe_pixel_and_result
    # 2) sum(lines coefficients through pixel) < pixel-expected-value - allowed_diff_between_targe_pixel_and_result
    for pixel_low_res_ind in range(num_of_low_res_pixels):
        print("setting constraints for low res pixel # ", pixel_low_res_ind, " out of ", num_of_low_res_pixels)
        pixel_low_res_x, pixel_low_res_y = ind2sub(image_low_res.shape, pixel_low_res_ind)

        constraint_1_name = "Constraint 1 for pixel " + str(pixel_low_res_ind)
        constraint_2_name = "Constraint 2 for pixel " + str(pixel_low_res_ind)

        #TODO this should be done for only the lines that pass through the pixel. how shall I know which ones?
        constraint_predicate_1 = lpSum([line_variables[str(j)] * lines_to_low_res_matrix[j][pixel_low_res_ind] for j in range(num_of_lines)]) \
                                 <= image_low_res[pixel_low_res_x][pixel_low_res_y] + allowed_diff_between_targe_pixel_and_result
        #TODO this should be done for only the lines that pass through the pixel. how shall I know which ones?
        constraint_predicate_2 = lpSum([line_variables[str(j)] * lines_to_low_res_matrix[j][pixel_low_res_ind] for j in range(num_of_lines)]) \
                                 >= image_low_res[pixel_low_res_x][pixel_low_res_y] - allowed_diff_between_targe_pixel_and_result

        problem += constraint_predicate_1, constraint_1_name
        problem += constraint_predicate_2, constraint_2_name

    #TODO make this path relative
    print("writing problem to file")
    problem.writeLP("D:/git/stav/python/threadart/linear_problems/problem.lp")
    print("solving problem")
    problem.solve(PULP_CBC_CMD(msg=0))

Я использовал lpSum и до сих пор создание ограничений занимает более часа. Также нет указаний на прогрессию решения.

У меня 4096 ограничений, каждое из которых содержит около 8500 переменных. Они в некоторой степени редки, но я нигде не упоминал об этом (Должен ли я?)

Пожалуйста, скажите мне, как я могу ускорить процесс создания проблемы к чему-то более реалистичному.

Я не возражаю против создания какого-либо файла, который Pulp будет читать сам (я заметил, что есть метод problem.write (), но не проблема.read())

Спасибо!