pyoptinterface-expert

name	pyoptinterface-expert
description	Specialized guidance for modeling mathematical optimization problems using the pyoptinterface_native library. Use when building optimization models (LP, QP, MIP, NLP), interfacing with solvers (Gurobi, HiGHS, Ipopt, etc.), and performing matrix-based or nonlinear modeling in Python.

PyOptInterface Expert Skill

This skill provides expert guidance for using pyoptinterface (POI), a high-performance Python modeling interface for mathematical optimization solvers.

Core Concepts

POI follows a common modeling pattern:

1. Create a solver-specific model (e.g., poi.highs.Model()).
2. Add variables using model.add_variable() or model.add_m_variables().
3. Set the objective using model.set_objective().
4. Add constraints using model.add_linear_constraint(), model.add_quadratic_constraint(), or model.add_nl_constraint().
5. Call model.optimize() and retrieve results.

Key Workflows

1. Basic Modeling

import pyoptinterface as poi
from pyoptinterface import highs

model = highs.Model()
x = model.add_variable(lb=0, name="x")
y = model.add_variable(lb=0, name="y")

model.set_objective(x + 2 * y, poi.ObjectiveSense.Minimize)
model.add_linear_constraint(x + y >= 10, name="c1")

model.optimize()
status = model.get_model_attribute(poi.ModelAttribute.TerminationStatus)
if status == poi.TerminationStatusCode.OPTIMAL:
    print(f"x: {model.get_variable_attribute(x, poi.VariableAttribute.Value)}")

2. Matrix-based Modeling

Use add_m_variables and add_m_linear_constraints for performance with NumPy/SciPy.

import numpy as np
x = model.add_m_variables((10,), lb=0)
A = np.random.rand(5, 10)
b = np.ones(5)
model.add_m_linear_constraints(A, x, poi.Leq, b)

3. Nonlinear Modeling

Nonlinear expressions must be wrapped in nl.graph() context.

from pyoptinterface import nl
with nl.graph():
    model.add_nl_objective(x * x + nl.sin(y))
    model.add_nl_constraint(x * y >= 5)

Reference Documentation

• API Reference: references/api.md
• Modeling Examples: references/examples.md

Best Practices

• Use poi.quicksum() for large summations.
• Check TerminationStatus before accessing variable values.
• For large-scale models, prefer the Matrix API (add_m_...).