Solve LP, MILP, NLP, MINLP, and DAE optimization models with Pyomo.
npx skills add https://github.com/sverzijl/planning_latest --skill pyomo
Please help me install this Agent Skill. Skill: pyomo Source: https://github.com/sverzijl/planning_latest/tree/main/.claude/skills/pyomo Command: npx skills add https://github.com/sverzijl/planning_latest --skill pyomo
Pyomo is a Python-based optimization modeling library that enables building, solving, and analyzing mathematical programs, removing the friction of switching between modeling languages and solvers.
Install Pyomo and a solver, then create a minimal Pyomo model in Python, solve it with a solver, and print the results. Example steps:
High-intent search queries and answers about installing and using this skill.
You can formulate and solve linear and mixed-integer optimization models in Python by defining variables, objectives, and constraints within a ConcreteModel, then invoking a solver like GLPK or CBC through a solver factory interface.
Yes, you can handle dynamic optimization and differential algebraic equations in Pyomo using the pyomo.dae module, which supports discretization and simulation of differential equations for dynamic system modeling.
Pyomo is compatible with open-source solvers like GLPK, CBC, and IPOPT, as well as commercial solvers like Gurobi and CPLEX for solving nonlinear and mixed-integer nonlinear programming problems.
Yes, Pyomo supports generalized disjunctive programming (GDP) and can be applied to networked production-distribution problems, guiding model formulation and advanced analysis for complex logical constraints.
Yes, you need to install Pyomo and at least one external solver separately, as the library provides interfaces to integrate with these solvers rather than bundling the solver algorithms directly.
The best way to start is installing Pyomo via pip, creating a simple ConcreteModel with two variables, defining an objective and a constraint, then solving it with a solver factory and displaying the results.
tondevrelFormulate and solve linear, mixed-integer, and non-linear optimization models with Pyomo.
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