Analysis

Learn Optimization & Convex Analysis

Convex sets and functions, linear programming and duality, gradient descent and Newton's method, Lagrange multipliers and the KKT conditions, Lagrangian duality, and the unifying power of convex optimization.

Free to start · adaptive placement finds your level · reviews timed so it stays learned.

What you'll learn

12 lessons in Optimization & Convex Analysis

Convex setsConvex functionsLinear programmingLP duality & the simplex methodGradient descentNewton's method for optimizationLagrange multipliersThe KKT conditionsLagrangian dualityConvex optimizationStochastic gradient descentSubgradients & non-smooth optimization
How Erudia teaches

Built to be understood — and remembered.

Every idea is taught with motivation and a worked example before the drills, and an FSRS spaced-repetition engine schedules each review for the moment just before you'd forget it. A short placement check finds what you already know, so you start Optimization & Convex Analysis exactly where it's useful.

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