Ideone.com

fork download

copy

from itertools import product, combinations_with_replacement
import time
import threading
from collections import defaultdict
import math
 
# 配置参数（可根据需要修改）
TARGET = 168000  # 目标值
BASE_VALUES = [36.5, 41.5, 59,68.5, 74, 91.5]  # 基础系数列表
FLUCTUATION = 1.0  # 系数波动范围
MAX_SOLUTIONS = 3  # 每个组合的最大解数量
SOLVER_TIMEOUT = 180  # 求解超时时间(秒)
THREE_VAR_THRESHOLD = 220000  # 使用三个变量的阈值
PRODUCT_RANGE_THRESHOLD = 148000  # 乘积范围限制阈值
HIGH_TARGET_THRESHOLD = 260000  # 更高目标值阈值
SHOW_PROGRESS = True  # 是否显示进度
MAX_SOLUTIONS_PER_COMB = 100  # 每个组合的最大解数量，用于提前终止
 
def is_valid_product(p):
    """检查单个乘积是否在有效范围内"""
    if TARGET > PRODUCT_RANGE_THRESHOLD:
        if TARGET > HIGH_TARGET_THRESHOLD:
            return p <= 115000  # 目标值超过260000时，仅限制最大值
        else:
            return 74000 <= p <= 115000  # 目标值在148000-260000之间时，使用原范围
    else:
        return True  # 目标值较小时不限制乘积范围
 
def find_single_variable_solutions(values):
    """查找单个数的解（a*x = TARGET）"""
    solutions = []
    for a in values:
        x = TARGET / a
        if x.is_integer() and 1 <= x <= 10000 and is_valid_product(a * x):
            solutions.append((a, x))
            if len(solutions) >= MAX_SOLUTIONS:
                return solutions  # 提前终止
    return solutions
 
def find_two_variable_solutions(values):
    """查找两个变量的解（a*x + b*y = TARGET）"""
    solutions = defaultdict(list)
 
    # 生成所有可能的(a, b)组合
    for a in values:
        for b in values:
            if a == b:  # 避免重复
                continue
 
            # 计算x的有效范围
            min_x = max(1, math.ceil((TARGET - b * 10000) / a))
            max_x = min(math.floor((TARGET - 1) / a), 10000)
 
            if max_x < min_x:
                continue
 
            for x in range(min_x, max_x + 1):
                remainder = TARGET - a * x
 
                # 检查remainder是否在可能的范围内
                if remainder < 1 or remainder > b * 10000:
                    continue
 
                # 检查remainder是否能被b整除
                if remainder % b == 0:
                    y = remainder // b
                    if 1 <= y <= 10000 and is_valid_product(b * y):
                        solutions[(a, b)].append((a, x, b, y))
                        if len(solutions[(a, b)]) >= MAX_SOLUTIONS_PER_COMB:
                            break  # 提前终止当前组合的搜索
 
    return solutions
 
def find_three_variable_solutions(values):
    """优化的三变量求解算法"""
    solutions = defaultdict(list)
 
    # 对系数进行排序，便于剪枝
    sorted_values = sorted(values)
 
    # 预计算每个系数的有效范围
    value_ranges = {}
    for a in sorted_values:
        if TARGET > PRODUCT_RANGE_THRESHOLD:
            if TARGET > HIGH_TARGET_THRESHOLD:
                min_x = max(1, math.ceil(1 / a))  # 取消下限，最小为1
                max_x = min(10000, math.floor(115000 / a))
            else:
                min_x = max(1, math.ceil(74000 / a))
                max_x = min(10000, math.floor(115000 / a))
        else:
            min_x = 1
            max_x = 10000
        value_ranges[a] = (min_x, max_x)
 
    total_combinations = len(sorted_values) * (len(sorted_values) - 1) * (len(sorted_values) - 2) // 6
    processed_combinations = 0
 
    # 三重循环，但添加了更多剪枝条件
    for i, a in enumerate(sorted_values):
        min_x, max_x = value_ranges[a]
 
        # 计算可能的x值数量，决定步长
        x_count = max_x - min_x + 1
        x_step = max(1, x_count // 1000)  # 自适应步长
 
        for x in range(min_x, max_x + 1, x_step):
            ax = a * x
            if not is_valid_product(ax):
                continue
 
            remainder1 = TARGET - ax
            if TARGET > PRODUCT_RANGE_THRESHOLD:
                if TARGET > HIGH_TARGET_THRESHOLD:
                    if remainder1 < 0:
                        continue
                else:
                    if remainder1 < 2 * 74000:
                        continue
 
            # 限制b的选择范围，避免重复组合
            for j in range(i + 1, len(sorted_values)):
                b = sorted_values[j]
 
                if TARGET > PRODUCT_RANGE_THRESHOLD:
                    if TARGET > HIGH_TARGET_THRESHOLD:
                        min_y = max(1, math.ceil(1 / b))  # 取消下限，最小为1
                        max_y = min(10000, math.floor(remainder1 / b))
                    else:
                        min_y = max(1, math.ceil(74000 / b))
                        max_y = min(10000, math.floor((remainder1 - 74000) / b))
                else:
                    min_y = 1
                    max_y = math.floor(remainder1 / b)
 
                if max_y < min_y:
                    continue
 
                # 计算可能的y值数量，决定步长
                y_count = max_y - min_y + 1
                y_step = max(1, y_count // 100)  # 自适应步长
 
                for y in range(min_y, max_y + 1, y_step):
                    by = b * y
                    if not is_valid_product(by):
                        continue
 
                    remainder2 = remainder1 - by
                    if TARGET > PRODUCT_RANGE_THRESHOLD:
                        if TARGET > HIGH_TARGET_THRESHOLD:
                            if remainder2 < 0 or remainder2 > 115000:
                                continue
                        else:
                            if remainder2 < 74000 or remainder2 > 115000:
                                continue
 
                    # 限制c的选择范围
                    found_solution = False
                    for k in range(j + 1, len(sorted_values)):
                        c = sorted_values[k]
 
                        # 检查remainder2是否能被c整除
                        if remainder2 % c != 0:
                            continue
 
                        z = remainder2 // c
                        if 1 <= z <= 10000 and is_valid_product(c * z):
                            key = (a, b, c)
                            solutions[key].append((a, x, b, y, c, z))
                            found_solution = True
                            if len(solutions[key]) >= MAX_SOLUTIONS_PER_COMB:
                                break  # 提前终止当前组合的搜索
 
                    # 如果找到解且达到最大数量，跳出y循环
                    if found_solution and len(solutions[key]) >= MAX_SOLUTIONS_PER_COMB:
                        break  # 跳出y循环
 
                # 进度显示
                if SHOW_PROGRESS and j % 10 == 0:
                    print(f"\r三变量组合进度: {processed_combinations}/{total_combinations} 组", end='')
                processed_combinations += 1
 
    if SHOW_PROGRESS:
        print(f"\r三变量组合进度: {processed_combinations}/{total_combinations} 组 - 完成")
 
    return solutions
 
def find_balanced_solutions(solutions, var_count, num=2):
    """从所有解中筛选出最平衡的解"""
    if var_count == 1 or not solutions:
        return solutions  # 单变量或无解时直接返回
 
    # 当目标值超过220000时，不计算平衡解
    if TARGET > THREE_VAR_THRESHOLD:
        return []
 
    # 计算解的平衡性（变量间差异最小）
    balanced = []
    for sol in solutions:
        vars = sol[1::2]  # 提取x, y, z
        diff = max(vars) - min(vars)
        balanced.append((diff, sol))
 
    # 按差异排序，取前num个
    return [s for _, s in sorted(balanced, key=lambda x: x[0])[:num]]
 
def find_original_solutions(solutions, balanced_solutions, num=3):
    """从剩余解中获取原始顺序的解"""
    if not solutions:
        return []
 
    # 排除已在平衡解中的项
    remaining = [s for s in solutions if s not in balanced_solutions]
    return remaining[:num]
 
def display_solutions(solutions_dict, var_count):
    """优化的解显示函数"""
    if not solutions_dict:
        return
 
    print(f"\n找到 {len(solutions_dict)} 组{var_count}变量解：")
 
    for i, (coeffs, pair_solutions) in enumerate(sorted(solutions_dict.items()), 1):
        # 当目标值超过220000时，不显示平衡解
        if TARGET > THREE_VAR_THRESHOLD:
            all_display = pair_solutions[:MAX_SOLUTIONS]
            print_tag = "[原始解]"
        else:
            # 计算平衡解和原始解
            balanced = find_balanced_solutions(pair_solutions, var_count)
            original = find_original_solutions(pair_solutions, balanced)
            all_display = balanced + original
 
        if var_count == 1:
            a = coeffs
            print(f"\n{i}. 组合: a={a} ({len(pair_solutions)} 个有效解)")
        elif var_count == 2:
            a, b = coeffs
            print(f"\n{i}. 组合: a={a}, b={b} ({len(pair_solutions)} 个有效解)")
        else:  # var_count == 3
            a, b, c = coeffs
            print(f"\n{i}. 组合: a={a}, b={b}, c={c} ({len(pair_solutions)} 个有效解)")
 
        for j, sol in enumerate(all_display, 1):
            if TARGET > THREE_VAR_THRESHOLD:
                tag = print_tag
            else:
                tag = "[平衡解]" if j <= len(balanced) else "[原始解]"
 
            if var_count == 1:
                a, x = sol
                print(f"  {j}. x={x}, a*x={a*x:.1f}, 总和={a*x:.1f} {tag}")
            elif var_count == 2:
                a, x, b, y = sol
                print(f"  {j}. x={x}, y={y}, a*x={a*x:.1f}, b*y={b*y:.1f}, 总和={a*x + b*y:.1f} {tag}")
            else:  # var_count == 3
                a, x, b, y, c, z = sol
                print(f"  {j}. x={x}, y={y}, z={z}, "
                      f"a*x={a*x:.1f}, b*y={b*y:.1f}, c*z={c*z:.1f}, "
                      f"总和={a*x + b*y + c*z:.1f} {tag}")
 
def run_with_timeout(func, args=(), kwargs=None, timeout=SOLVER_TIMEOUT):
    """运行函数并设置超时限制"""
    if kwargs is None:
        kwargs = {}
 
    result = []
    error = []
 
    def wrapper():
        try:
            result.append(func(*args, **kwargs))
        except Exception as e:
            error.append(e)
 
    thread = threading.Thread(target=wrapper)
    thread.daemon = True
    thread.start()
    thread.join(timeout)
 
    if thread.is_alive():
        print(f"警告: {func.__name__} 超时（{timeout}秒），跳过此方法")
        return None
 
    if error:
        raise error[0]
 
    return result[0]
 
def main():
    print(f"目标值: {TARGET}")
 
    # 生成波动后的系数
    FLUCTUATED_VALUES = [round(v - FLUCTUATION, 1) for v in BASE_VALUES]
 
    # 尝试基础系数
    print(f"\n==== 尝试基础系数 ====")
 
    # 检查目标值是否超过阈值
    if TARGET > THREE_VAR_THRESHOLD:
        print(f"目标值 {TARGET} 超过阈值 {THREE_VAR_THRESHOLD}，只尝试三变量解")
        base_solutions = {
            'single': [],
            'two': [],
            'three': run_with_timeout(find_three_variable_solutions, args=(BASE_VALUES,))
        }
 
        # 显示三变量解
        if base_solutions['three'] and len(base_solutions['three']) > 0:
            display_solutions(base_solutions['three'], 3)
            print(f"\n使用基础系数列表，共找到有效解")
            return
        else:
            print("\n基础系数三变量无解，尝试波动系数")
    else:
        # 目标值未超过阈值，按顺序尝试单、双、三变量解
        base_solutions = {
            'single': run_with_timeout(find_single_variable_solutions, args=(BASE_VALUES,)),
            'two': run_with_timeout(find_two_variable_solutions, args=(BASE_VALUES,)),
            'three': []
        }
 
        # 检查是否有解
        has_solution = False
 
        # 显示单变量解
        if base_solutions['single']:
            has_solution = True
            display_solutions({a: [sol] for a, sol in zip(BASE_VALUES, base_solutions['single']) if sol}, 1)
 
        # 显示双变量解
        if base_solutions['two'] and len(base_solutions['two']) > 0:
            has_solution = True
            display_solutions(base_solutions['two'], 2)
 
        # 单变量和双变量都无解时，尝试三变量解
        if not has_solution:
            print(f"\n==== 单变量和双变量无解，尝试三变量解 ====")
            base_solutions['three'] = run_with_timeout(find_three_variable_solutions, args=(BASE_VALUES,))
 
            if base_solutions['three'] and len(base_solutions['three']) > 0:
                has_solution = True
                display_solutions(base_solutions['three'], 3)
 
        # 如果找到解，退出程序
        if has_solution:
            print(f"\n使用基础系数列表，共找到有效解")
            return
 
    # 如果基础系数没有找到解，尝试波动系数
    print(f"\n==== 尝试波动系数 ====")
 
    # 检查目标值是否超过阈值
    if TARGET > THREE_VAR_THRESHOLD:
        print(f"目标值 {TARGET} 超过阈值 {THREE_VAR_THRESHOLD}，只尝试三变量解")
        fluctuated_solutions = {
            'single': [],
            'two': [],
            'three': run_with_timeout(find_three_variable_solutions, args=(FLUCTUATED_VALUES,))
        }
 
        # 显示三变量解
        if fluctuated_solutions['three'] and len(fluctuated_solutions['three']) > 0:
            display_solutions(fluctuated_solutions['three'], 3)
            print(f"\n使用波动系数列表，共找到有效解")
            return
    else:
        # 目标值未超过阈值，按顺序尝试单、双、三变量解
        fluctuated_solutions = {
            'single': run_with_timeout(find_single_variable_solutions, args=(FLUCTUATED_VALUES,)),
            'two': run_with_timeout(find_two_variable_solutions, args=(FLUCTUATED_VALUES,)),
            'three': []
        }
 
        # 重置标志
        has_solution = False
 
        # 显示单变量解
        if fluctuated_solutions['single']:
            has_solution = True
            display_solutions({a: [sol] for a, sol in zip(FLUCTUATED_VALUES, fluctuated_solutions['single']) if sol}, 1)
 
        # 显示双变量解
        if fluctuated_solutions['two'] and len(fluctuated_solutions['two']) > 0:
            has_solution = True
            display_solutions(fluctuated_solutions['two'], 2)
 
        # 单变量和双变量都无解时，尝试三变量解
        if not has_solution:
            print(f"\n==== 单变量和双变量无解，尝试三变量解 ====")
            fluctuated_solutions['three'] = run_with_timeout(find_three_variable_solutions, args=(FLUCTUATED_VALUES,))
 
            if fluctuated_solutions['three'] and len(fluctuated_solutions['three']) > 0:
                has_solution = True
                display_solutions(fluctuated_solutions['three'], 3)
 
        # 如果找到解，退出程序
        if has_solution:
            print(f"\n使用波动系数列表，共找到有效解")
            return
 
    # 如果所有系数集都没有找到解
    print("\n没有找到符合条件的解，即使使用波动后的系数列表。")
 
if __name__ == "__main__":
    start_time = time.time()
    main()
    print(f"\n总耗时: {time.time() - start_time:.2f}秒")

from itertools import product, combinations_with_replacement
import time
import threading
from collections import defaultdict
import math

# 配置参数（可根据需要修改）
TARGET = 168000  # 目标值
BASE_VALUES = [36.5, 41.5, 59,68.5, 74, 91.5]  # 基础系数列表
FLUCTUATION = 1.0  # 系数波动范围
MAX_SOLUTIONS = 3  # 每个组合的最大解数量
SOLVER_TIMEOUT = 180  # 求解超时时间(秒)
THREE_VAR_THRESHOLD = 220000  # 使用三个变量的阈值
PRODUCT_RANGE_THRESHOLD = 148000  # 乘积范围限制阈值
HIGH_TARGET_THRESHOLD = 260000  # 更高目标值阈值
SHOW_PROGRESS = True  # 是否显示进度
MAX_SOLUTIONS_PER_COMB = 100  # 每个组合的最大解数量，用于提前终止

def is_valid_product(p):
    """检查单个乘积是否在有效范围内"""
    if TARGET > PRODUCT_RANGE_THRESHOLD:
        if TARGET > HIGH_TARGET_THRESHOLD:
            return p <= 115000  # 目标值超过260000时，仅限制最大值
        else:
            return 74000 <= p <= 115000  # 目标值在148000-260000之间时，使用原范围
    else:
        return True  # 目标值较小时不限制乘积范围

def find_single_variable_solutions(values):
    """查找单个数的解（a*x = TARGET）"""
    solutions = []
    for a in values:
        x = TARGET / a
        if x.is_integer() and 1 <= x <= 10000 and is_valid_product(a * x):
            solutions.append((a, x))
            if len(solutions) >= MAX_SOLUTIONS:
                return solutions  # 提前终止
    return solutions

def find_two_variable_solutions(values):
    """查找两个变量的解（a*x + b*y = TARGET）"""
    solutions = defaultdict(list)
    
    # 生成所有可能的(a, b)组合
    for a in values:
        for b in values:
            if a == b:  # 避免重复
                continue
                
            # 计算x的有效范围
            min_x = max(1, math.ceil((TARGET - b * 10000) / a))
            max_x = min(math.floor((TARGET - 1) / a), 10000)
            
            if max_x < min_x:
                continue
                
            for x in range(min_x, max_x + 1):
                remainder = TARGET - a * x
                
                # 检查remainder是否在可能的范围内
                if remainder < 1 or remainder > b * 10000:
                    continue
                    
                # 检查remainder是否能被b整除
                if remainder % b == 0:
                    y = remainder // b
                    if 1 <= y <= 10000 and is_valid_product(b * y):
                        solutions[(a, b)].append((a, x, b, y))
                        if len(solutions[(a, b)]) >= MAX_SOLUTIONS_PER_COMB:
                            break  # 提前终止当前组合的搜索
    
    return solutions

def find_three_variable_solutions(values):
    """优化的三变量求解算法"""
    solutions = defaultdict(list)
    
    # 对系数进行排序，便于剪枝
    sorted_values = sorted(values)
    
    # 预计算每个系数的有效范围
    value_ranges = {}
    for a in sorted_values:
        if TARGET > PRODUCT_RANGE_THRESHOLD:
            if TARGET > HIGH_TARGET_THRESHOLD:
                min_x = max(1, math.ceil(1 / a))  # 取消下限，最小为1
                max_x = min(10000, math.floor(115000 / a))
            else:
                min_x = max(1, math.ceil(74000 / a))
                max_x = min(10000, math.floor(115000 / a))
        else:
            min_x = 1
            max_x = 10000
        value_ranges[a] = (min_x, max_x)
    
    total_combinations = len(sorted_values) * (len(sorted_values) - 1) * (len(sorted_values) - 2) // 6
    processed_combinations = 0
    
    # 三重循环，但添加了更多剪枝条件
    for i, a in enumerate(sorted_values):
        min_x, max_x = value_ranges[a]
        
        # 计算可能的x值数量，决定步长
        x_count = max_x - min_x + 1
        x_step = max(1, x_count // 1000)  # 自适应步长
        
        for x in range(min_x, max_x + 1, x_step):
            ax = a * x
            if not is_valid_product(ax):
                continue
                
            remainder1 = TARGET - ax
            if TARGET > PRODUCT_RANGE_THRESHOLD:
                if TARGET > HIGH_TARGET_THRESHOLD:
                    if remainder1 < 0:
                        continue
                else:
                    if remainder1 < 2 * 74000:
                        continue
                
            # 限制b的选择范围，避免重复组合
            for j in range(i + 1, len(sorted_values)):
                b = sorted_values[j]
                
                if TARGET > PRODUCT_RANGE_THRESHOLD:
                    if TARGET > HIGH_TARGET_THRESHOLD:
                        min_y = max(1, math.ceil(1 / b))  # 取消下限，最小为1
                        max_y = min(10000, math.floor(remainder1 / b))
                    else:
                        min_y = max(1, math.ceil(74000 / b))
                        max_y = min(10000, math.floor((remainder1 - 74000) / b))
                else:
                    min_y = 1
                    max_y = math.floor(remainder1 / b)
                    
                if max_y < min_y:
                    continue
                    
                # 计算可能的y值数量，决定步长
                y_count = max_y - min_y + 1
                y_step = max(1, y_count // 100)  # 自适应步长
                
                for y in range(min_y, max_y + 1, y_step):
                    by = b * y
                    if not is_valid_product(by):
                        continue
                        
                    remainder2 = remainder1 - by
                    if TARGET > PRODUCT_RANGE_THRESHOLD:
                        if TARGET > HIGH_TARGET_THRESHOLD:
                            if remainder2 < 0 or remainder2 > 115000:
                                continue
                        else:
                            if remainder2 < 74000 or remainder2 > 115000:
                                continue
                        
                    # 限制c的选择范围
                    found_solution = False
                    for k in range(j + 1, len(sorted_values)):
                        c = sorted_values[k]
                        
                        # 检查remainder2是否能被c整除
                        if remainder2 % c != 0:
                            continue
                            
                        z = remainder2 // c
                        if 1 <= z <= 10000 and is_valid_product(c * z):
                            key = (a, b, c)
                            solutions[key].append((a, x, b, y, c, z))
                            found_solution = True
                            if len(solutions[key]) >= MAX_SOLUTIONS_PER_COMB:
                                break  # 提前终止当前组合的搜索
                    
                    # 如果找到解且达到最大数量，跳出y循环
                    if found_solution and len(solutions[key]) >= MAX_SOLUTIONS_PER_COMB:
                        break  # 跳出y循环
                            
                # 进度显示
                if SHOW_PROGRESS and j % 10 == 0:
                    print(f"\r三变量组合进度: {processed_combinations}/{total_combinations} 组", end='')
                processed_combinations += 1
    
    if SHOW_PROGRESS:
        print(f"\r三变量组合进度: {processed_combinations}/{total_combinations} 组 - 完成")
    
    return solutions

def find_balanced_solutions(solutions, var_count, num=2):
    """从所有解中筛选出最平衡的解"""
    if var_count == 1 or not solutions:
        return solutions  # 单变量或无解时直接返回
    
    # 当目标值超过220000时，不计算平衡解
    if TARGET > THREE_VAR_THRESHOLD:
        return []
    
    # 计算解的平衡性（变量间差异最小）
    balanced = []
    for sol in solutions:
        vars = sol[1::2]  # 提取x, y, z
        diff = max(vars) - min(vars)
        balanced.append((diff, sol))
    
    # 按差异排序，取前num个
    return [s for _, s in sorted(balanced, key=lambda x: x[0])[:num]]

def find_original_solutions(solutions, balanced_solutions, num=3):
    """从剩余解中获取原始顺序的解"""
    if not solutions:
        return []
    
    # 排除已在平衡解中的项
    remaining = [s for s in solutions if s not in balanced_solutions]
    return remaining[:num]

def display_solutions(solutions_dict, var_count):
    """优化的解显示函数"""
    if not solutions_dict:
        return
    
    print(f"\n找到 {len(solutions_dict)} 组{var_count}变量解：")
    
    for i, (coeffs, pair_solutions) in enumerate(sorted(solutions_dict.items()), 1):
        # 当目标值超过220000时，不显示平衡解
        if TARGET > THREE_VAR_THRESHOLD:
            all_display = pair_solutions[:MAX_SOLUTIONS]
            print_tag = "[原始解]"
        else:
            # 计算平衡解和原始解
            balanced = find_balanced_solutions(pair_solutions, var_count)
            original = find_original_solutions(pair_solutions, balanced)
            all_display = balanced + original
        
        if var_count == 1:
            a = coeffs
            print(f"\n{i}. 组合: a={a} ({len(pair_solutions)} 个有效解)")
        elif var_count == 2:
            a, b = coeffs
            print(f"\n{i}. 组合: a={a}, b={b} ({len(pair_solutions)} 个有效解)")
        else:  # var_count == 3
            a, b, c = coeffs
            print(f"\n{i}. 组合: a={a}, b={b}, c={c} ({len(pair_solutions)} 个有效解)")
        
        for j, sol in enumerate(all_display, 1):
            if TARGET > THREE_VAR_THRESHOLD:
                tag = print_tag
            else:
                tag = "[平衡解]" if j <= len(balanced) else "[原始解]"
            
            if var_count == 1:
                a, x = sol
                print(f"  {j}. x={x}, a*x={a*x:.1f}, 总和={a*x:.1f} {tag}")
            elif var_count == 2:
                a, x, b, y = sol
                print(f"  {j}. x={x}, y={y}, a*x={a*x:.1f}, b*y={b*y:.1f}, 总和={a*x + b*y:.1f} {tag}")
            else:  # var_count == 3
                a, x, b, y, c, z = sol
                print(f"  {j}. x={x}, y={y}, z={z}, "
                      f"a*x={a*x:.1f}, b*y={b*y:.1f}, c*z={c*z:.1f}, "
                      f"总和={a*x + b*y + c*z:.1f} {tag}")

def run_with_timeout(func, args=(), kwargs=None, timeout=SOLVER_TIMEOUT):
    """运行函数并设置超时限制"""
    if kwargs is None:
        kwargs = {}
    
    result = []
    error = []
    
    def wrapper():
        try:
            result.append(func(*args, **kwargs))
        except Exception as e:
            error.append(e)
    
    thread = threading.Thread(target=wrapper)
    thread.daemon = True
    thread.start()
    thread.join(timeout)
    
    if thread.is_alive():
        print(f"警告: {func.__name__} 超时（{timeout}秒），跳过此方法")
        return None
    
    if error:
        raise error[0]
    
    return result[0]

def main():
    print(f"目标值: {TARGET}")
    
    # 生成波动后的系数
    FLUCTUATED_VALUES = [round(v - FLUCTUATION, 1) for v in BASE_VALUES]
    
    # 尝试基础系数
    print(f"\n==== 尝试基础系数 ====")
    
    # 检查目标值是否超过阈值
    if TARGET > THREE_VAR_THRESHOLD:
        print(f"目标值 {TARGET} 超过阈值 {THREE_VAR_THRESHOLD}，只尝试三变量解")
        base_solutions = {
            'single': [],
            'two': [],
            'three': run_with_timeout(find_three_variable_solutions, args=(BASE_VALUES,))
        }
        
        # 显示三变量解
        if base_solutions['three'] and len(base_solutions['three']) > 0:
            display_solutions(base_solutions['three'], 3)
            print(f"\n使用基础系数列表，共找到有效解")
            return
        else:
            print("\n基础系数三变量无解，尝试波动系数")
    else:
        # 目标值未超过阈值，按顺序尝试单、双、三变量解
        base_solutions = {
            'single': run_with_timeout(find_single_variable_solutions, args=(BASE_VALUES,)),
            'two': run_with_timeout(find_two_variable_solutions, args=(BASE_VALUES,)),
            'three': []
        }
        
        # 检查是否有解
        has_solution = False
        
        # 显示单变量解
        if base_solutions['single']:
            has_solution = True
            display_solutions({a: [sol] for a, sol in zip(BASE_VALUES, base_solutions['single']) if sol}, 1)
        
        # 显示双变量解
        if base_solutions['two'] and len(base_solutions['two']) > 0:
            has_solution = True
            display_solutions(base_solutions['two'], 2)
        
        # 单变量和双变量都无解时，尝试三变量解
        if not has_solution:
            print(f"\n==== 单变量和双变量无解，尝试三变量解 ====")
            base_solutions['three'] = run_with_timeout(find_three_variable_solutions, args=(BASE_VALUES,))
            
            if base_solutions['three'] and len(base_solutions['three']) > 0:
                has_solution = True
                display_solutions(base_solutions['three'], 3)
        
        # 如果找到解，退出程序
        if has_solution:
            print(f"\n使用基础系数列表，共找到有效解")
            return
    
    # 如果基础系数没有找到解，尝试波动系数
    print(f"\n==== 尝试波动系数 ====")
    
    # 检查目标值是否超过阈值
    if TARGET > THREE_VAR_THRESHOLD:
        print(f"目标值 {TARGET} 超过阈值 {THREE_VAR_THRESHOLD}，只尝试三变量解")
        fluctuated_solutions = {
            'single': [],
            'two': [],
            'three': run_with_timeout(find_three_variable_solutions, args=(FLUCTUATED_VALUES,))
        }
        
        # 显示三变量解
        if fluctuated_solutions['three'] and len(fluctuated_solutions['three']) > 0:
            display_solutions(fluctuated_solutions['three'], 3)
            print(f"\n使用波动系数列表，共找到有效解")
            return
    else:
        # 目标值未超过阈值，按顺序尝试单、双、三变量解
        fluctuated_solutions = {
            'single': run_with_timeout(find_single_variable_solutions, args=(FLUCTUATED_VALUES,)),
            'two': run_with_timeout(find_two_variable_solutions, args=(FLUCTUATED_VALUES,)),
            'three': []
        }
        
        # 重置标志
        has_solution = False
        
        # 显示单变量解
        if fluctuated_solutions['single']:
            has_solution = True
            display_solutions({a: [sol] for a, sol in zip(FLUCTUATED_VALUES, fluctuated_solutions['single']) if sol}, 1)
        
        # 显示双变量解
        if fluctuated_solutions['two'] and len(fluctuated_solutions['two']) > 0:
            has_solution = True
            display_solutions(fluctuated_solutions['two'], 2)
        
        # 单变量和双变量都无解时，尝试三变量解
        if not has_solution:
            print(f"\n==== 单变量和双变量无解，尝试三变量解 ====")
            fluctuated_solutions['three'] = run_with_timeout(find_three_variable_solutions, args=(FLUCTUATED_VALUES,))
            
            if fluctuated_solutions['three'] and len(fluctuated_solutions['three']) > 0:
                has_solution = True
                display_solutions(fluctuated_solutions['three'], 3)
        
        # 如果找到解，退出程序
        if has_solution:
            print(f"\n使用波动系数列表，共找到有效解")
            return
    
    # 如果所有系数集都没有找到解
    print("\n没有找到符合条件的解，即使使用波动后的系数列表。")

if __name__ == "__main__":
    start_time = time.time()
    main()
    print(f"\n总耗时: {time.time() - start_time:.2f}秒")    

Success #stdin #stdout 0.06s 10700KB

stdin

copy

Standard input is empty

stdout

copy

目标值: 168000

==== 尝试基础系数 ====

找到 30 组2变量解：

1. 组合: a=36.5, b=41.5 (13 个有效解)
  1. x=2173, y=2137.0, a*x=79314.5, b*y=88685.5, 总和=168000.0 [平衡解]
  2. x=2090, y=2210.0, a*x=76285.0, b*y=91715.0, 总和=168000.0 [平衡解]
  3. x=1509, y=2721.0, a*x=55078.5, b*y=112921.5, 总和=168000.0 [原始解]
  4. x=1592, y=2648.0, a*x=58108.0, b*y=109892.0, 总和=168000.0 [原始解]
  5. x=1675, y=2575.0, a*x=61137.5, b*y=106862.5, 总和=168000.0 [原始解]

2. 组合: a=36.5, b=59 (10 个有效解)
  1. x=1816, y=1724.0, a*x=66284.0, b*y=101716.0, 总和=168000.0 [平衡解]
  2. x=1698, y=1797.0, a*x=61977.0, b*y=106023.0, 总和=168000.0 [平衡解]
  3. x=1462, y=1943.0, a*x=53363.0, b*y=114637.0, 总和=168000.0 [原始解]
  4. x=1580, y=1870.0, a*x=57670.0, b*y=110330.0, 总和=168000.0 [原始解]
  5. x=1934, y=1651.0, a*x=70591.0, b*y=97409.0, 总和=168000.0 [原始解]

3. 组合: a=36.5, b=68.5 (9 个有效解)
  1. x=1600, y=1600.0, a*x=58400.0, b*y=109600.0, 总和=168000.0 [平衡解]
  2. x=1463, y=1673.0, a*x=53399.5, b*y=114600.5, 总和=168000.0 [平衡解]
  3. x=1737, y=1527.0, a*x=63400.5, b*y=104599.5, 总和=168000.0 [原始解]
  4. x=1874, y=1454.0, a*x=68401.0, b*y=99599.0, 总和=168000.0 [原始解]
  5. x=2011, y=1381.0, a*x=73401.5, b*y=94598.5, 总和=168000.0 [原始解]

4. 组合: a=36.5, b=74 (7 个有效解)
  1. x=1588, y=1487.0, a*x=57962.0, b*y=110038.0, 总和=168000.0 [平衡解]
  2. x=1736, y=1414.0, a*x=63364.0, b*y=104636.0, 总和=168000.0 [平衡解]
  3. x=1884, y=1341.0, a*x=68766.0, b*y=99234.0, 总和=168000.0 [原始解]
  4. x=2032, y=1268.0, a*x=74168.0, b*y=93832.0, 总和=168000.0 [原始解]
  5. x=2180, y=1195.0, a*x=79570.0, b*y=88430.0, 总和=168000.0 [原始解]

5. 组合: a=36.5, b=91.5 (6 个有效解)
  1. x=1587, y=1203.0, a*x=57925.5, b*y=110074.5, 总和=168000.0 [平衡解]
  2. x=1770, y=1130.0, a*x=64605.0, b*y=103395.0, 总和=168000.0 [平衡解]
  3. x=1953, y=1057.0, a*x=71284.5, b*y=96715.5, 总和=168000.0 [原始解]
  4. x=2136, y=984.0, a*x=77964.0, b*y=90036.0, 总和=168000.0 [原始解]
  5. x=2319, y=911.0, a*x=84643.5, b*y=83356.5, 总和=168000.0 [原始解]

6. 组合: a=41.5, b=36.5 (13 个有效解)
  1. x=2137, y=2173.0, a*x=88685.5, b*y=79314.5, 总和=168000.0 [平衡解]
  2. x=2210, y=2090.0, a*x=91715.0, b*y=76285.0, 总和=168000.0 [平衡解]
  3. x=1334, y=3086.0, a*x=55361.0, b*y=112639.0, 总和=168000.0 [原始解]
  4. x=1407, y=3003.0, a*x=58390.5, b*y=109609.5, 总和=168000.0 [原始解]
  5. x=1480, y=2920.0, a*x=61420.0, b*y=106580.0, 总和=168000.0 [原始解]

7. 组合: a=41.5, b=59 (8 个有效解)
  1. x=1728, y=1632.0, a*x=71712.0, b*y=96288.0, 总和=168000.0 [平衡解]
  2. x=1610, y=1715.0, a*x=66815.0, b*y=101185.0, 总和=168000.0 [平衡解]
  3. x=1374, y=1881.0, a*x=57021.0, b*y=110979.0, 总和=168000.0 [原始解]
  4. x=1492, y=1798.0, a*x=61918.0, b*y=106082.0, 总和=168000.0 [原始解]
  5. x=1846, y=1549.0, a*x=76609.0, b*y=91391.0, 总和=168000.0 [原始解]

8. 组合: a=41.5, b=68.5 (7 个有效解)
  1. x=1465, y=1565.0, a*x=60797.5, b*y=107202.5, 总和=168000.0 [平衡解]
  2. x=1602, y=1482.0, a*x=66483.0, b*y=101517.0, 总和=168000.0 [平衡解]
  3. x=1328, y=1648.0, a*x=55112.0, b*y=112888.0, 总和=168000.0 [原始解]
  4. x=1739, y=1399.0, a*x=72168.5, b*y=95831.5, 总和=168000.0 [原始解]
  5. x=1876, y=1316.0, a*x=77854.0, b*y=90146.0, 总和=168000.0 [原始解]

9. 组合: a=41.5, b=74 (7 个有效解)
  1. x=1468, y=1447.0, a*x=60922.0, b*y=107078.0, 总和=168000.0 [平衡解]
  2. x=1320, y=1530.0, a*x=54780.0, b*y=113220.0, 总和=168000.0 [平衡解]
  3. x=1616, y=1364.0, a*x=67064.0, b*y=100936.0, 总和=168000.0 [原始解]
  4. x=1764, y=1281.0, a*x=73206.0, b*y=94794.0, 总和=168000.0 [原始解]
  5. x=1912, y=1198.0, a*x=79348.0, b*y=88652.0, 总和=168000.0 [原始解]

10. 组合: a=41.5, b=91.5 (5 个有效解)
  1. x=1398, y=1202.0, a*x=58017.0, b*y=109983.0, 总和=168000.0 [平衡解]
  2. x=1581, y=1119.0, a*x=65611.5, b*y=102388.5, 总和=168000.0 [平衡解]
  3. x=1764, y=1036.0, a*x=73206.0, b*y=94794.0, 总和=168000.0 [原始解]
  4. x=1947, y=953.0, a*x=80800.5, b*y=87199.5, 总和=168000.0 [原始解]
  5. x=2130, y=870.0, a*x=88395.0, b*y=79605.0, 总和=168000.0 [原始解]

11. 组合: a=59, b=36.5 (10 个有效解)
  1. x=1578, y=2052.0, a*x=93102.0, b*y=74898.0, 总和=168000.0 [平衡解]
  2. x=1505, y=2170.0, a*x=88795.0, b*y=79205.0, 总和=168000.0 [平衡解]
  3. x=921, y=3114.0, a*x=54339.0, b*y=113661.0, 总和=168000.0 [原始解]
  4. x=994, y=2996.0, a*x=58646.0, b*y=109354.0, 总和=168000.0 [原始解]
  5. x=1067, y=2878.0, a*x=62953.0, b*y=105047.0, 总和=168000.0 [原始解]

12. 组合: a=59, b=41.5 (8 个有效解)
  1. x=1549, y=1846.0, a*x=91391.0, b*y=76609.0, 总和=168000.0 [平衡解]
  2. x=1466, y=1964.0, a*x=86494.0, b*y=81506.0, 总和=168000.0 [平衡解]
  3. x=968, y=2672.0, a*x=57112.0, b*y=110888.0, 总和=168000.0 [原始解]
  4. x=1051, y=2554.0, a*x=62009.0, b*y=105991.0, 总和=168000.0 [原始解]
  5. x=1134, y=2436.0, a*x=66906.0, b*y=101094.0, 总和=168000.0 [原始解]

13. 组合: a=59, b=68.5 (5 个有效解)
  1. x=1366, y=1276.0, a*x=80594.0, b*y=87406.0, 总和=168000.0 [平衡解]
  2. x=1229, y=1394.0, a*x=72511.0, b*y=95489.0, 总和=168000.0 [平衡解]
  3. x=955, y=1630.0, a*x=56345.0, b*y=111655.0, 总和=168000.0 [原始解]
  4. x=1092, y=1512.0, a*x=64428.0, b*y=103572.0, 总和=168000.0 [原始解]
  5. x=1503, y=1158.0, a*x=88677.0, b*y=79323.0, 总和=168000.0 [原始解]

14. 组合: a=59, b=74 (9 个有效解)
  1. x=1232, y=1288, a*x=72688.0, b*y=95312.0, 总和=168000.0 [平衡解]
  2. x=1306, y=1229, a*x=77054.0, b*y=90946.0, 总和=168000.0 [平衡解]
  3. x=936, y=1524, a*x=55224.0, b*y=112776.0, 总和=168000.0 [原始解]
  4. x=1010, y=1465, a*x=59590.0, b*y=108410.0, 总和=168000.0 [原始解]
  5. x=1084, y=1406, a*x=63956.0, b*y=104044.0, 总和=168000.0 [原始解]

15. 组合: a=59, b=91.5 (4 个有效解)
  1. x=1095, y=1130.0, a*x=64605.0, b*y=103395.0, 总和=168000.0 [平衡解]
  2. x=1278, y=1012.0, a*x=75402.0, b*y=92598.0, 总和=168000.0 [平衡解]
  3. x=912, y=1248.0, a*x=53808.0, b*y=114192.0, 总和=168000.0 [原始解]
  4. x=1461, y=894.0, a*x=86199.0, b*y=81801.0, 总和=168000.0 [原始解]

16. 组合: a=68.5, b=36.5 (8 个有效解)
  1. x=1308, y=2148.0, a*x=89598.0, b*y=78402.0, 总和=168000.0 [平衡解]
  2. x=1235, y=2285.0, a*x=84597.5, b*y=83402.5, 总和=168000.0 [平衡解]
  3. x=797, y=3107.0, a*x=54594.5, b*y=113405.5, 总和=168000.0 [原始解]
  4. x=870, y=2970.0, a*x=59595.0, b*y=108405.0, 总和=168000.0 [原始解]
  5. x=943, y=2833.0, a*x=64595.5, b*y=103404.5, 总和=168000.0 [原始解]

17. 组合: a=68.5, b=41.5 (7 个有效解)
  1. x=1316, y=1876.0, a*x=90146.0, b*y=77854.0, 总和=168000.0 [平衡解]
  2. x=1233, y=2013.0, a*x=84460.5, b*y=83539.5, 总和=168000.0 [平衡解]
  3. x=818, y=2698.0, a*x=56033.0, b*y=111967.0, 总和=168000.0 [原始解]
  4. x=901, y=2561.0, a*x=61718.5, b*y=106281.5, 总和=168000.0 [原始解]
  5. x=984, y=2424.0, a*x=67404.0, b*y=100596.0, 总和=168000.0 [原始解]

18. 组合: a=68.5, b=59 (5 个有效解)
  1. x=1276, y=1366.0, a*x=87406.0, b*y=80594.0, 总和=168000.0 [平衡解]
  2. x=1158, y=1503.0, a*x=79323.0, b*y=88677.0, 总和=168000.0 [平衡解]
  3. x=804, y=1914.0, a*x=55074.0, b*y=112926.0, 总和=168000.0 [原始解]
  4. x=922, y=1777.0, a*x=63157.0, b*y=104843.0, 总和=168000.0 [原始解]
  5. x=1040, y=1640.0, a*x=71240.0, b*y=96760.0, 总和=168000.0 [原始解]

19. 组合: a=68.5, b=74 (4 个有效解)
  1. x=1140, y=1215.0, a*x=78090.0, b*y=89910.0, 总和=168000.0 [平衡解]
  2. x=1288, y=1078.0, a*x=88228.0, b*y=79772.0, 总和=168000.0 [平衡解]
  3. x=844, y=1489.0, a*x=57814.0, b*y=110186.0, 总和=168000.0 [原始解]
  4. x=992, y=1352.0, a*x=67952.0, b*y=100048.0, 总和=168000.0 [原始解]

20. 组合: a=68.5, b=91.5 (3 个有效解)
  1. x=1050, y=1050.0, a*x=71925.0, b*y=96075.0, 总和=168000.0 [平衡解]
  2. x=867, y=1187.0, a*x=59389.5, b*y=108610.5, 总和=168000.0 [平衡解]
  3. x=1233, y=913.0, a*x=84460.5, b*y=83539.5, 总和=168000.0 [原始解]

21. 组合: a=74, b=36.5 (8 个有效解)
  1. x=1268, y=2032.0, a*x=93832.0, b*y=74168.0, 总和=168000.0 [平衡解]
  2. x=1195, y=2180.0, a*x=88430.0, b*y=79570.0, 总和=168000.0 [平衡解]
  3. x=757, y=3068.0, a*x=56018.0, b*y=111982.0, 总和=168000.0 [原始解]
  4. x=830, y=2920.0, a*x=61420.0, b*y=106580.0, 总和=168000.0 [原始解]
  5. x=903, y=2772.0, a*x=66822.0, b*y=101178.0, 总和=168000.0 [原始解]

22. 组合: a=74, b=41.5 (6 个有效解)
  1. x=1198, y=1912.0, a*x=88652.0, b*y=79348.0, 总和=168000.0 [平衡解]
  2. x=1115, y=2060.0, a*x=82510.0, b*y=85490.0, 总和=168000.0 [平衡解]
  3. x=783, y=2652.0, a*x=57942.0, b*y=110058.0, 总和=168000.0 [原始解]
  4. x=866, y=2504.0, a*x=64084.0, b*y=103916.0, 总和=168000.0 [原始解]
  5. x=949, y=2356.0, a*x=70226.0, b*y=97774.0, 总和=168000.0 [原始解]

23. 组合: a=74, b=59 (9 个有效解)
  1. x=1229, y=1306, a*x=90946.0, b*y=77054.0, 总和=168000.0 [平衡解]
  2. x=1170, y=1380, a*x=86580.0, b*y=81420.0, 总和=168000.0 [平衡解]
  3. x=757, y=1898, a*x=56018.0, b*y=111982.0, 总和=168000.0 [原始解]
  4. x=816, y=1824, a*x=60384.0, b*y=107616.0, 总和=168000.0 [原始解]
  5. x=875, y=1750, a*x=64750.0, b*y=103250.0, 总和=168000.0 [原始解]

24. 组合: a=74, b=68.5 (4 个有效解)
  1. x=1215, y=1140.0, a*x=89910.0, b*y=78090.0, 总和=168000.0 [平衡解]
  2. x=1078, y=1288.0, a*x=79772.0, b*y=88228.0, 总和=168000.0 [平衡解]
  3. x=804, y=1584.0, a*x=59496.0, b*y=108504.0, 总和=168000.0 [原始解]
  4. x=941, y=1436.0, a*x=69634.0, b*y=98366.0, 总和=168000.0 [原始解]

25. 组合: a=74, b=91.5 (3 个有效解)
  1. x=1014, y=1016.0, a*x=75036.0, b*y=92964.0, 总和=168000.0 [平衡解]
  2. x=1197, y=868.0, a*x=88578.0, b*y=79422.0, 总和=168000.0 [平衡解]
  3. x=831, y=1164.0, a*x=61494.0, b*y=106506.0, 总和=168000.0 [原始解]

26. 组合: a=91.5, b=36.5 (6 个有效解)
  1. x=984, y=2136.0, a*x=90036.0, b*y=77964.0, 总和=168000.0 [平衡解]
  2. x=911, y=2319.0, a*x=83356.5, b*y=84643.5, 总和=168000.0 [平衡解]
  3. x=619, y=3051.0, a*x=56638.5, b*y=111361.5, 总和=168000.0 [原始解]
  4. x=692, y=2868.0, a*x=63318.0, b*y=104682.0, 总和=168000.0 [原始解]
  5. x=765, y=2685.0, a*x=69997.5, b*y=98002.5, 总和=168000.0 [原始解]

27. 组合: a=91.5, b=41.5 (5 个有效解)
  1. x=953, y=1947.0, a*x=87199.5, b*y=80800.5, 总和=168000.0 [平衡解]
  2. x=870, y=2130.0, a*x=79605.0, b*y=88395.0, 总和=168000.0 [平衡解]
  3. x=621, y=2679.0, a*x=56821.5, b*y=111178.5, 总和=168000.0 [原始解]
  4. x=704, y=2496.0, a*x=64416.0, b*y=103584.0, 总和=168000.0 [原始解]
  5. x=787, y=2313.0, a*x=72010.5, b*y=95989.5, 总和=168000.0 [原始解]

28. 组合: a=91.5, b=59 (4 个有效解)
  1. x=1012, y=1278.0, a*x=92598.0, b*y=75402.0, 总和=168000.0 [平衡解]
  2. x=894, y=1461.0, a*x=81801.0, b*y=86199.0, 总和=168000.0 [平衡解]
  3. x=658, y=1827.0, a*x=60207.0, b*y=107793.0, 总和=168000.0 [原始解]
  4. x=776, y=1644.0, a*x=71004.0, b*y=96996.0, 总和=168000.0 [原始解]

29. 组合: a=91.5, b=68.5 (3 个有效解)
  1. x=913, y=1233.0, a*x=83539.5, b*y=84460.5, 总和=168000.0 [平衡解]
  2. x=776, y=1416.0, a*x=71004.0, b*y=96996.0, 总和=168000.0 [平衡解]
  3. x=639, y=1599.0, a*x=58468.5, b*y=109531.5, 总和=168000.0 [原始解]

30. 组合: a=91.5, b=74 (3 个有效解)
  1. x=1016, y=1014.0, a*x=92964.0, b*y=75036.0, 总和=168000.0 [平衡解]
  2. x=868, y=1197.0, a*x=79422.0, b*y=88578.0, 总和=168000.0 [平衡解]
  3. x=720, y=1380.0, a*x=65880.0, b*y=102120.0, 总和=168000.0 [原始解]

使用基础系数列表，共找到有效解

总耗时: 0.02秒

https://ideone.com/GWD5aa

language:

Python 3 nbc (python 3.7.3)

created:

visibility:

public

Share or Embed source code

Discover > Sphere Engine API

The brand new service which powers Ideone!

Discover > IDE Widget

Widget for compiling and running the source code in a web browser!

Discover > Sphere Engine API

Discover > IDE Widget

Choose your language