B01: Linear Programming

  • A crash course in Linear Programming

  • Stigler's diet: Optimal nutrition

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B04: Optimal Assignment

  • Introduction to Optimal Transport

  • Optimal Assignment problem

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B07a: Semi-Discrete OT

  • Semi-Discrete Optimal Transport

  • Voronoi Tesselations

  • Aurenhammer's method

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B09: Matching estimation

  • The Choo and Siow model

  • The marriage market

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B02: Networks

  • Network Topology

  • Min-cost flow problem

  • The shortest-path problem

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B05: Entropic Regularization

  • Entropic regularization

  • Gradient Descent

  • IPFP/Sinkhorn

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B07b: Characteristics model

  • The pure characteristics model

  • BLP random coefficient model

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B10: The Gravity equation

  • The gravity equation

  • Predicting trade flows

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B03: Dynamic Programming

  • A crash course in Dynamic Programming

  • Markov processes

  • Algorithms for DP

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B06: Random Utility

  • EMax operator

  • Daly-Sachary-Williams theorem

  • The MEV class

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B08: Discrete choice

  • Estimation with and without observable heterogeneity

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B11: Large-Scale computations

  • PyTorch

  • Lazy Tensors

  • Keops

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External resources