GAMSPy and Machine Learning

GAMSPy heralds a new era of possibilities, bridging the gap between machine learning and optimization that was previously difficult to cross using GAMS alone. Here’s why GAMSPy is the ultimate choice:

• Easy to understand, easy to write

  • eq1[...] = y == tanh(a @ W + b)

• Versatility in Solver Selection

  • regression.solve(solver="your_favourite_solver")

• It provides a robust algebraic language that allows you to experiment with how a neural network is implemented.

• Built-in flexibility:

  • You are not limited to inference; you can also train your neural network.

  • You can build the architecture from scratch using GAMSPy

• Development speed of Python combined with model generation speed of the GAMS execution engine

  • Equations and variables are generated in GAMS, not in Python, giving GAMSPy a speed advantage.

We are continually developing our ML-related features. If you have specific needs or require additional information, please use our Discourse platform.

• Matrix Operations - Introduction
• NN Formulations (ReLU, Conv2d…)
• Embedding a Trained Neural Network in GAMSPy
• Regression Example
• Binary Classification Example
• Neural Network Example