5.8.3. Modeling and Optimization Based on MaplPy¶
In this section, we will demonstrate how to use MaplPy, the Python API extension package of MindOpt APL, to model and invoke MindOpt to solve the problem in Example of Non-linear Programming.
You can refer to the section MindOpt APL for more details of MindOpt APL. For a complete API introduction of MaplPy, you can refer to the MaplPy Manual.
First, create a .mapl file named mapl_lr.mapl and write the mathematical form of the problem to be optimized.
# 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;
In the Python code, import the MaplPy package
import maplpy as mp
Create a new MAPL model named “lr”
m = mp.MAPL('lr') # Create MAPL model
Read the declared mathematical form into the model
m.read(modelfile) # Read model file
Specify the MindOpt solver and set its corresponding path
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
Invoke the solving function, which will automatically construct the optimization model and call MindOpt to solve it.
m.solve() # Solve
Execute the Python code, and the log of the MindOpt solving process is as follows
[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.
After successfully solving the problem, the MAPL model will obtain the optimal solution to the problem. We can retrieve its value through the API and print it:
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))
This will output
x = -0.5728551903261085
y = -0.8196564705817261
z = 1.2728750741453017
The example MaplPy-LR.py provides the complete Python source code:
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))