Master the mathematics of finding the best through 24 interactive demonstrations. From convex sets and gradient descent to LP duality, simulated annealing, and portfolio optimization.
See also: Linear Algebra for convex geometry and rank-based constraints, Machine Learning for gradient-based methods that train every model, and Game Theory for finding equilibria via constraint satisfaction.
Convex sets, convex functions, and Jensen's inequality
Feasible regions, the simplex method, and LP duality
Learning rates, momentum, and Adam optimizer
Lagrange multipliers and KKT conditions
Convex programs, duality gap, and interior point methods
Branch and bound, traveling salesman, and knapsack
Loss landscapes, saddle points, and simulated annealing
Linear regression, portfolio optimization, and optimal transport