数学建模入门笔记(1) Python pulp库解线性规划问题_用pulp包求解线性规划问题

参考：Python求解线性规划——PuLP使用教程 - Only(AR) - 博客园 (cnblogs.com)

1.Define the model

model = pl.LpProblem(name=""，sense=pl.LpMaximize)
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name 模型的名字

sense 模型的类型（pl.LpMaximize/pl.LpMinimize）

2.Define the decision variables

用x[i]存储变量，命名为xi

x = {i: pl.LpVariable(name=f"x{i}", lowBound=,upBound=, cat=) for i in range(1,)}
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name 变量名

lowBound 变量最小值（默认负无穷）

upBound 变量最大值 （默认正无穷）

cat 变量的类型（pl.Binary 0/1,pl.Integer 整数,pl.Continuous 实数（默认值））

3.Set the objective

目标函数不用括号

model += x[1]+x[2]+x[3]
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4.Add constraints

约束式用括号括起来

式子可以是>=,<=,==，不能是>,<,=

model += (2*x[1]+3*x[2]+x[3]>=10)
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5.Solve the optimization problem

status = model.solve()
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6.Print the results

print(f"status: {pl.LpStatus[model.status]}")
print(f"objective: {model.objective.value()}")
 
for var in model.variables():
    print(f"{var.name}: {var.value()}")
 
for name, constraint in model.constraints.items():
    print(f"{name}: {constraint.value()}")
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*完整代码

《数学建模算法与应用》司守奎、孙兆亮 例1.2

import pulp as pl
model = pl.LpProblem(name="problem",sense=pl.LpMaximize)
x = {i:pl.LpVariable(name=f"x{i}",lowBound=0,cat=pl.LpContinuous) for i in range(1,4)}
model += (2 * x[1] - 5 * x[2] + x[3] >= 10)
model += (x[1] + 3 * x[2] + x[3] <= 12)
model += (x[1] + x[2] + x[3] == 7)
model += 2 * x[1] + 3 * x[2] -5 * x[3]
status = model.solve()
print(f"status:{pl.LpStatus[model.status]}")
print(f"objective:{model.objective.value()}")
for var in model.variables():
    print(f"{var.name}:{var.value()}")
for name,constraint in model.constraints.items():
    print(f"{name}:{constraint.value()}")
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