最新最易上手IntelliJ IDEA配置CPLEX详细步骤_寒冢人家的博客-CSDN博客_cplex1263本站上关于Java IDE进行线性规划求解问题的文章非常少，对于CPLEX求解器的安装和使用也很难找到保姆级步骤说明，鉴于此，笔者在探索java求解线性规划问题的准备阶段，也遇到了一些问题，在此将安装成功的经验介绍如下：一、CPLEX安装1.CPLEX安装包下载由于IBM公司的ILOG CPLEX Optimization Studio是收费的，即使在官网上下载免费的学术版也需要使用你单位（或学校）的邮箱，而且你的你的单位必须在IBM上注册过域名，否则也无法下载，所以这里给出笔者千辛万苦下好的1https://blog.csdn.net/weixin_52459924/article/details/123615165?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522165280109516781683993822%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=165280109516781683993822&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-22-123615165-null-null.142%5Ev10%5Epc_search_result_control_group,157%5Ev4%5Econtrol&utm_term=ilog+cplex+optimization&spm=1018.2226.3001.4187cplex python安装及入门_VickyVivan的博客-CSDN博客_cplex python安装cplex python安装及入门1. cplex安装2. cplex学习资源3. 百度网盘资源笔者最近学习cplex，从安装到入门走了不少弯路，现在提供一个相对全面的cplex学习博文。1. cplex安装可以参考这篇博文DOcplex系列（二）——怎样成功安装和调用学术版DOcplex里面介绍了学术版cplex的安装以及在python下的调用。学术版cplex12.8网盘资源将在文末提供该文仅介绍了在python下的安装。因为anaconda统一管理包更方便，而pycharm直接使用这些包即https://blog.csdn.net/VickyVivan/article/details/116430220?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522165280134916782390513169%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=165280134916782390513169&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-1-116430220-null-null.142%5Ev10%5Epc_search_result_control_group,157%5Ev4%5Econtrol&utm_term=ilog+cplex+optimization%2Bpycharm&spm=1018.2226.3001.4187
cplex：链接：https://pan.baidu.com/s/1g0fmMdpgk4fP-px8sG8eKg
提取码：kr85
测试gurobipy
from gurobipy import *
try:
# Create a new model
m = Model("mip1")
# Create variables
x = m.addVar(vtype=GRB.BINARY, name="x")
y = m.addVar(vtype=GRB.BINARY, name="y")
z = m.addVar(vtype=GRB.BINARY, name="z")
# Set objective
m.setObjective(x + y + 2 * z, GRB.MAXIMIZE)
# Add constraint: x + 2 y + 3 z <= 4
m.addConstr(x + 2 * y + 3 * z <= 4, "c0")
# Add constraint: x + y >= 1
m.addConstr(x + y >= 1, "c1")
m.optimize()
for v in m.getVars():
print(v.varName, v.x)
print('Obj:', m.objVal)
except GurobiError:
print('Error reported')
测试cplex
# -*- coding: utf-8 -*-
# The MIP problem solved in this example is:
#
# Maximize x1 + 2 x2 + 3 x3 + x4
# Subject to
# - x1 + x2 + x3 + 10 x4 <= 20
# x1 - 3 x2 + x3 <= 30
# x2 - 3.5x4 = 0
# Bounds
# 0 <= x1 <= 40
# 0 <= x2
# 0 <= x3
# 2 <= x4 <= 3
# Integers
# x4
import cplex
from cplex.exceptions import CplexError
# data common to all populateby functions
my_obj = [1.0, 2.0, 3.0, 1.0]
my_ub = [40.0, cplex.infinity, cplex.infinity, 3.0]
my_lb = [0.0, 0.0, 0.0, 2.0]
my_ctype = "CCCI"
my_colnames = ["x1", "x2", "x3", "x4"]
my_rhs = [20.0, 30.0, 0.0]
my_rownames = ["r1", "r2", "r3"]
my_sense = "LLE"
def populatebyrow(prob):
prob.objective.set_sense(prob.objective.sense.maximize)
prob.variables.add(obj=my_obj, lb=my_lb, ub=my_ub, types=my_ctype,
names=my_colnames)
rows = [[["x1", "x2", "x3", "x4"], [-1.0, 1.0, 1.0, 10.0]],
[["x1", "x2", "x3"], [1.0, -3.0, 1.0]],
[["x2", "x4"], [1.0, -3.5]]]
prob.linear_constraints.add(lin_expr=rows, senses=my_sense,
rhs=my_rhs, names=my_rownames)
try:
my_prob = cplex.Cplex()
handle = populatebyrow(my_prob)
my_prob.solve()
except CplexError as exc:
print(exc)
print()
# solution.get_status() returns an integer code
print("Solution status = ", my_prob.solution.get_status(), ":", end=' ')
# the following line prints the corresponding string
print(my_prob.solution.status[my_prob.solution.get_status()])
print("Solution value = ", my_prob.solution.get_objective_value())
numcols = my_prob.variables.get_num()
numrows = my_prob.linear_constraints.get_num()
slack = my_prob.solution.get_linear_slacks()
x = my_prob.solution.get_values()
print('x: ')
print(x)
