Covers types of errors in programming and emphasizes the importance of debugging and optimization techniques.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores gossip efficiency in decentralized systems, covering protocols, interaction needs, and bandwidth optimization, along with search algorithms and optimizations.
