combinatorial-optimization
2 free lessons tagged combinatorial-optimization across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Beyond Greedy: Minimization, Non-Monotone, and Richer Constraints
The monotone, cardinality-constrained case is only the entrance. This lesson maps the rest: why submodular minimization is easy while maximization is hard, how to maximize non-monotone functions, what to do under matroid and knapsack constraints, and where submodularity keeps appearing across machine learning.
Submodularity: The Mathematics of Diminishing Returns
Many selection problems share one property: each new item helps less than the last. Formalized, that property is submodularity, and it is what makes otherwise intractable problems solvable with guarantees. This lesson defines it precisely, shows the everyday examples, and explains why it is called the discrete analog of convexity.