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Resource > Training

MILP Ch.3: Linear Programming Formulations

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Resource > Training

MILP Ch.4: Linear Programming Formulation With Gurobi Python API

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Resource > Training

MILP Ch.5: Jupyter Notebook-1 Resource Assignment Problem Formulation

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Resource > Training

MILP Ch.6: Perfect Formulation Resource Assignment Problem (RAP)

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Resource > Training

MILP Ch.7: Jupyter Notebook-2 Perfect Formulation Resource Assignment Problem

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Resource > Training

MILP Ch.8: Methods for Solving MIP Problems

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Resource > Training

MILP Ch.9: Approach 1 Branch And Bound Methods For Solving MIP Problems Part 1

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Resource > Training

Mixed-Integer Programming (MIP) – A Primer on the Basics

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Resource

The Gurobi Python Modeling and Development Environment

When building an optimization model, one must choose from among two alternatives: Using Gurobi with a proprietary modeling language such as AMPL or GAMS, or using Gurobi with a full programming language such as C, C++, C#, Java, Python, VB, MATLAB or R.

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