pyomo-6-10-1

name	pyomo-6-10-1
description	Pyomo 6.10.1 — Python optimization modeling for LP, MIP, NLP, MINLP, DAE/optimal control, GDP (disjunctive programming), MPEC (equilibrium/complementarity), piecewise functions, network flow, stochastic programming, and more. Use this skill whenever the user mentions Pyomo, optimization modeling, mathematical programming, linear/nonlinear/mixed-integer programming, optimal control, differential equations optimization, disjunctive constraints, complementarity conditions, solver interfaces (Gurobi, CPLEX, IPOPT, GLPK, CBC, etc.), AMPL-style modeling in Python, or building optimization models with Python. Also use when the user asks about formulating LP/MIP/NLP models, network flow problems, facility location, lot sizing, transportation problems, diet/nutrition problems, parameter estimation, reactor design, job shop scheduling, or any mathematical optimization task in Python.
metadata	{"tags":["optimization","modeling","lp","mip","nlp","dae","gdp","mpec"]}

pyomo 6.10.1

Pyomo is a Python-based, open-source optimization modeling language that supports LP, MIP, NLP, MINLP, DAE, GDP, and MPEC formulations. It works with many solvers (Gurobi, CPLEX, GLPK, CBC, IPOPT, etc.) via SolverFactory.

Overview

Pyomo provides two modeling paradigms:

• ConcreteModel — Define model structure and data together in Python. Best for prototyping and small-to-medium models where data is available at construction time.
• AbstractModel — Define model structure first, then load data from .dat files or Python dicts. Best for larger models with external data sources or AMPL-style workflows.

Core components: Set, Param, Var, Objective, Constraint, Block. Indexed variants accept sets as arguments (e.g., Var(I, J) creates variables indexed by (i, j)).

Usage

Quick Start

import pyomo.environ as pyo

# Concrete model — inline data
m = pyo.ConcreteModel()
m.x = pyo.Var(bounds=(0, None))
m.y = pyo.Var(within=pyo.Binary)
m.obj = pyo.Objective(expr=2*m.x + 3*m.y, sense=pyo.minimize)
m.c1 = pyo.Constraint(expr=m.x + m.y >= 5)
m.c2 = pyo.Constraint(expr=m.x <= 10)

# Solve
opt = pyo.SolverFactory('glpk')
results = opt.solve(m)

# Inspect
print(pyo.value(m.x), pyo.value(m.y))

Abstract Model with Data

m = pyo.AbstractModel()
m.I = pyo.Set()
m.J = pyo.Set()
m.a = pyo.Param(m.I)
m.b = pyo.Param(m.J)
m.x = pyo.Var(m.I, m.J, within=pyo.NonNegativeReals)

def supply_rule(model, i):
    return sum(model.x[i, j] for j in model.J) <= model.a[i]

m.supply = pyo.Constraint(m.I, rule=supply_rule)

# Load data from .dat file or dict
instance = m.create_instance('data.dat')
# Or: instance = m.create_instance(data={None: {'I': {None: [1,2]}, ...}})

Common Patterns

• Summation: sum(model.x[i] for i in model.I) — use generator expressions, not pyo.summation() (deprecated).
• Indexed constraints: pyo.Constraint(I, rule=rule_func) creates one constraint per index.
• Skip rules: Return pyo.Constraint.Skip to skip constructing a constraint for certain indices.
• Inequality shorthand: pyo.inequality(lower, expr, upper) produces lower <= expr <= upper.
• Fixing variables: m.x[1].fix(5.0) sets value and marks as fixed; m.x[1].unfix() restores.

Solver Selection

# Common solvers by problem type:
opt = pyo.SolverFactory('glpk')    # LP (open source)
opt = pyo.SolverFactory('cbc')     # MIP (open source)
opt = pyo.SolverFactory('gurobi')  # LP/MIP/MINLP (commercial, fast)
opt = pyo.SolverFactory('cplex')   # LP/MIP/MILP (commercial)
opt = pyo.SolverFactory('ipopt')   # NLP/MINLP (open source)
opt = pyo.SolverFactory('neos.gurobi')  # Free remote solver via NEOS server

results = opt.solve(m, tee=True)   # tee=True prints solver output

Data Loading Options

• .dat files — AMPL data format, load with create_instance('file.dat')
• Python dicts — Pass directly to create_instance(data={None: {...}})
• CSV/Excel — Read with pandas or csv module, construct Params from DataFrames
• JSON — Parse with json module, convert to Pyomo data structures

Inspecting Models and Solutions

m.pprint()                    # Print full model structure
m.x.display()                 # Display variable values
results.write()               # Print solver results summary
print(results.solver.status)  # ok, error, etc.
print(results.solver.termination_condition)  # optimal, infeasible, etc.

# Collect duals/slacks from solver
m.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
m.slack = pyo.Suffix(direction=pyo.Suffix.IMPORT)
opt.solve(m)
print(m.dual[m.c1])           # Dual value for constraint c1

Gotchas

• ConcreteModel vs AbstractModel: Use ConcreteModel when all data is available in Python. Use AbstractModel only when loading from external .dat files or large data sources. Abstract models require create_instance() before solving.
• pyo.value() is essential: Always wrap Pyomo expressions with pyo.value() when you need a numeric value for comparison, printing, or Python arithmetic. m.x[1] returns a Pyomo object; pyo.value(m.x[1]) returns the float.
• Rule functions must accept model as first arg: For indexed components, the rule signature is rule(model, index). Non-indexed rules use rule(model).
• Generator expressions vs lists: Use generator expressions (sum(x[i] for i in I)) not list comprehensions (sum([x[i] for i in I])) — generators are faster and use less memory.
• Constraint.Skip vs returning None: Return pyo.Constraint.Skip to omit a constraint for an index. Returning None will cause an error.
• Variable domains: within=pyo.Binary is different from bounds=(0,1). Binary variables are integer; bounded continuous variables can take any value in [0,1]. Use pyo.Binary for true binary variables.
• Nonlinear expressions: Pyomo automatically detects nonlinear constraints and routes them to NLP-capable solvers. But open-source LP solvers (GLPK) cannot handle nonlinear constraints — use IPOPT or a commercial solver.
• SOS2 constraints: Only supported by some solvers (CPLEX, Gurobi). GLPK does not support SOS2 natively.
• GDP transformations: Disjunctive models must be transformed before solving. Common transforms: BigM, Hull, GDPopt. Not all solvers support all transforms.
• DAE discretization: DAE models require a discretization transform (e.g., finite differences or collocation) before solving. Use pyomo.dae.FiniteDifference or pyomo.dae.Collocation.
• Suffix direction: Use to collect solver results (duals, slacks). Use to send data to the solver. Some solvers don't support all suffix types.

References

• 01-core-modeling — Sets, Params, Vars, Objectives, Constraints, Abstract vs Concrete
• 02-lp-mip — Linear and mixed-integer programming: diet, transport, knapsack, facility location, lot sizing
• 03-nlp-minlp — Nonlinear programming: reactor design, parameter estimation, multimodal optimization
• 04-dae-optimal-control — DAE modeling, optimal control, discretization (finite difference, collocation), path constraints
• 05-gdp-disjunctive — Generalized disjunctive programming: disjunctions, transformations (BigM, Hull), job shop scheduling, facility layout
• 06-mpec-complementarity — Mathematical programs with equilibrium constraints: complementarity conditions, KKT-based formulations
• 07-piecewise-sos — Piecewise linear functions, SOS1/SOS2 constraints, special ordered sets
• 08-data-loading — Data loading: .dat files, dicts, pandas/CSV/Excel, JSON, parameter initialization patterns
• 09-solvers-results — Solver interfaces, result inspection, suffixes (duals/slacks), solver options, NEOS server
• 10-network-flow — Network flow: max flow, min cost flow, shortest path, transportation, network interdiction
• 11-advanced-patterns — Benders decomposition, column generation, callbacks, transforms, kernel API, performance tips