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amplpy is an interface that allows developers to access the features of AMPL from within Python. For a quick introduction to AMPL see Quick Introduction to AMPL.
amplpy
In the same way that AMPL’s syntax matches naturally the mathematical description of the model, the input and output data matches naturally Python lists, sets, dictionaries, pandas and numpy objects.
pandas
numpy
All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is sent and read back from AMPL, the size of the expanded model as such is irrelevant.
With amplpy you can model and solve large scale optimization problems in Python with the performance of heavily optimized C code without losing model readability. The same model can be deployed on applications built on different languages by just switching the API used.
Quick Start using Pandas dataframes
Data can be loaded in various forms, one of which is pandas.DataFrame objects.
pandas.DataFrame
Quick Start using lists and dictionaries
Data can be loaded in various forms, including Python lists and dictionaries.
Many more notebooks with examples are available on the AMPL Model Colaboratory and the new book Hands-On Optimization with AMPL in Python 🐍.
# Install Python API for AMPL $ python -m pip install amplpy --upgrade # Install solver modules (e.g., HiGHS, CBC, Gurobi) $ python -m amplpy.modules install highs cbc gurobi # Activate your license (e.g., free https://ampl.com/ce license) $ python -m amplpy.modules activate <license-uuid> # Import in Python $ python >>> from amplpy import AMPL >>> ampl = AMPL() # instantiate AMPL object
Note
You can use a free Community Edition license, which allows free and perpetual use of AMPL with Open-Source solvers.
# Minimal example: from amplpy import AMPL import pandas as pd ampl = AMPL() ampl.eval(r""" set A ordered; param S{A, A}; param lb default 0; param ub default 1; var w{A} >= lb <= ub; minimize portfolio_variance: sum {i in A, j in A} w[i] * S[i, j] * w[j]; s.t. portfolio_weights: sum {i in A} w[i] = 1; """) tickers, cov_matrix = # ... pre-process data in Python ampl.set["A"] = tickers ampl.param["S"] = pd.DataFrame( cov_matrix, index=tickers, columns=tickers ) ampl.option["solver"] = "gurobi" ampl.option["gurobi_options"] = "outlev=1" ampl.solve() assert ampl.solve_result == "solved" sigma = ampl.get_value("sqrt(sum {i in A, j in A} w[i] * S[i, j] * w[j])") print(f"Volatility: {sigma*100:.1f}%") # ... post-process solution in Python
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Introduction