Dynamic Programming: Fibonacci Numbers

Related lectures (30)

Page 3 of 3

Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.

Explores optimization problems and greedy algorithms to find the best solutions efficiently.

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Explores optimization problems solved with greedy algorithms and proves the optimality of the Cashier's Algorithm for U.S. coins.

Explores microeconomic consumer theory, utility maximization, optimization problems, and decision-making models.

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Explains the process of finding an initial Basic Feasible Solution for linear optimization problems using the Simplex Algorithm.

Covers asset pricing, hedging, American claims, stopping times, and dynamic programming in finance.

Explores binary search, merge sort, recursion in algorithms, Fibonacci numbers, and dynamic programming.

Covers the concept of linear optimization and the duality relationship between primal and dual problems.