Introduces the concept of shortest path, discussing weighted paths, Hamiltonian paths, and path optimization algorithms.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers finding the shortest path in directed graphs efficiently using algorithmic approaches and discussing related NP-complete problems.
Explores stochastic softmax tricks, reparametrization, and argmax, addressing challenges in expectation estimation and gradient variance.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
