Gyorgy Matyasfalvi (15 notebooks)#
AMPL Development Tutorial 1/6 – Capacitated Facility Location Problem#
Description: This notebook marks the beginning of a six-part series.
AMPL Development Tutorial 2/6 – Stochastic Capacitated Facility Location Problem#
Description: This notebook continues our six-part series as the second installment.
AMPL Development Tutorial 3/6 – Benders Decomposition via AMPL scripting#
Description: In this third installment of our six-part series, we continue our exploration by addressing the complexities introduced by the stochastic programming formulation presented in part two.
AMPL Development Tutorial 4/6 – Benders Decomposition via PYTHON scripting#
Description: In this fourth installment of our six-part series, we advance our exploration by demonstrating how to adapt our AMPL script for use with AMPL’s Python API.
AMPL Development Tutorial 5/6 – Parallelizing Subproblem Solves in Benders Decomposition#
Notebooks > AMPL Development Tutorial 5/6 – Parallelizing Subproblem Solves in Benders Decomposition
Description: In the fifth installment of our six-part series, we delve deeper by showing how to evolve our Benders decomposition Python script from a serial execution to one that solves subproblems in parallel.
AMPL Development Tutorial 6/6 – Implementing Benders Decomposition with ampls#
Description: This concluding notebook in our six-part series delves into enhancing the efficiency of our decomposition algorithm by utilizing AMPL Solver Libraries (ampls).
Capacity expansion of power generation#
Description: Models the extensive form of a deterministic multi-stage capacity expansion problem. In this model we can have multiple resources of the same type which have identical properties. The model can be further developed into a stochastic one.
Debugging Model Infeasibility#
Description: This notebook offers a concise guide on troubleshooting model infeasibility using AMPL’s presolve feature and other language capabilities.
Introduction to Linear and Integer Programming#
Description: Basic introduction to linear programming and AMPL via a lemonade stand example
Introduction to Mathematical Optimization#
Description: Basic introduction to optimization and AMPL via unconstrained optimization
Network Linear Programs#
Description: Basic introduction to network linear programms and AMPL via max flow and shortest path problems
Plot feasible region#
Description: Plot the feasible region and optimal solution for a simple two variable model using AMPL’s Python API.
Pricing and target-market#
Description: Formulate a pricing optimization and target-market problem as a MILP.
Production Model: lemonade stand example#
Description: Basic introduction to AMPL’s indexed entities and the Pygwalker Python package via a lemonade stand example
Unit Commitment for Electrical Power Generation#
Description: This notebook illustrates the power generation problem using AMPL. The original version featured the Gurobi solver. By default, this notebook uses the HiGHS and CBC solvers.