5.8.3. 基于MaplPy的问题建模及优化¶
在本节中，我们将展示如何使用 MindOpt APL 的Python API扩展包建模并调用 MindOpt , 求解 非线性规划问题示例 中的问题。
目前，MindOpt APL 支持云平台及本地版两种使用方式，可参考 MindOpt APL 的建模与优化 章节的介绍。 MaplPy 的完整API介绍，可以参考 MaplPy 手册。
首先，创建.mapl文件，编写待优化问题的数学形式。
# Example: 2D Regularized Logistic Regression Classifcation
#
# data points:
# - label=1: {(1,2), (3,0)}
# - label=-1: {(-1,1),(3,-1)}
var x;
var y;
var z;
minimize
ln(1+exp(x+2*y+z)) + ln(1+exp(x-y-z)) + ln(1+exp(-3*x+y-z)) + ln(1+exp(3*x+z));
subto
x^2 + y^2 <= 1;
在Python代码中，导入 MaplPy 依赖包
import maplpy as mp
创建一个新的 MAPL 模型，命名为“lr”
m = mp.MAPL('lr') # Create MAPL model
读入我们声明的数学形式到模型中
m.read(modelfile) # Read model file
指定 MindOpt 求解器，并设定其对应的路径地址
m.setOption(mp.StrOption.SOLVER, "mindopt")
m.setOption(mp.StrOption.SOLVER_PATH, "/home/mindopt/mindopt/2.1.0/linux64-x86/bin") #you can set your own solver-path here
调用模型的求解函数，这会自动实现优化模型的构建及 MindOpt 的调用求解。
m.solve() # Solve
执行Python代码，MindOpt 求解过程的日志如下
[mindopt@idec mindopt] python mapl_lr.py
Running mindoptampl
MindOpt Version 2.1.0 (Build date: xxxxxxxx)
Copyright (c) 2020-2025 Alibaba Cloud.
Start license validation (current time : xxxxxxxx).
License validation terminated. Time : 0.002s
wantsol=1
Model summary.
- Num. variables : 3
- Num. constraints : 1
- Num. nonzeros : 8
- Bound range : [1.00e+00,1.00e+00]
Interior point method started.
Iter PrimObj DualObj PrimFea DualFea GapFea Mu Time
0 +2.77258874e+00 +3.77258875e+00 0.0e+00 1.0e+00 1.0e+00 1.0e+00 0.00s
1 +2.10107174e+00 +2.23028984e+00 0.0e+00 7.8e-01 1.0e-01 1.0e-01 0.00s
2 +1.42075610e+00 +1.52543264e+00 6.7e-01 2.7e-01 1.8e-03 1.8e-03 0.00s
3 +1.65885758e+00 +1.72802441e+00 0.0e+00 1.2e-01 3.6e-02 3.6e-02 0.00s
4 +1.65511048e+00 +1.69576276e+00 0.0e+00 6.6e-02 2.1e-02 2.1e-02 0.00s
5 +1.65736811e+00 +1.66000850e+00 0.0e+00 2.6e-04 2.6e-03 2.6e-03 0.00s
6 +1.65490062e+00 +1.65506485e+00 0.0e+00 9.0e-06 1.6e-04 1.6e-04 0.00s
7 +1.65474536e+00 +1.65474726e+00 0.0e+00 3.6e-08 1.9e-06 1.9e-06 0.00s
8 +1.65474358e+00 +1.65474367e+00 0.0e+00 4.9e-12 9.1e-08 9.1e-08 0.00s
Terminated.
- Method : Interior point method.
- Primal objective : 1.6547435766246e+00
- Dual objective : 1.6547436675411e+00
- Num. threads : 1
- Num. iterations : 8
- Solver details : Solver terminated with a primal/dual optimal status.
Interior point method terminated. Time : 0.004203s
Optimizer summary.
- Optimizer used : Interior point method
- Optimizer status : OPTIMAL
Solution summary. Primal solution
- Objective : +1.6547435766e+00
OPTIMAL; objective 1.654744
Completed.
成功求解后， MAPL 模型会获取到问题的最优解 (optimal solution)，我们可以通过API获取其值并打印：
x = m.getVariable("x").value
y = m.getVariable("y").value
z = m.getVariable("z").value
print("x = {}".format(x))
print("y = {}".format(y))
print("z = {}".format(z))
这会输出
x = -0.5728551903261085
y = -0.8196564705817261
z = 1.2728750741453017
示例 MaplPy-LR.py 提供了完整的 Python 源代码：
import maplpy as mp
def lr_solve(modelfile):
m = mp.MAPL('lr') # Create MAPL model
m.read(modelfile) # Read model file
m.setOption(mp.StrOption.SOLVER, "mindopt")
m.setOption(mp.StrOption.SOLVER_PATH, "/home/mindopt/mindopt/2.1.0/linux64-x86/bin") #you can set your own solver-path here
m.solve() # Solve
return m
m = lr_solve("mapl_lr.mapl")
x = m.getVariable("x").value
y = m.getVariable("y").value
z = m.getVariable("z").value
print("x = {}".format(x))
print("y = {}".format(y))
print("z = {}".format(z))