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Lecture
Dynamic Programming: Fibonacci Numbers
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Related lectures (40)
Dynamic Programming: Introduction and Fibonacci Numbers
Introduces Dynamic Programming, focusing on saving computation by remembering previous calculations and applying it to solve optimization problems efficiently.
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Optimization with Constraints: KKT Conditions
Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.
Optimization Techniques: Convexity in Machine Learning
Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.
Approximation Algorithms
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Dynamic Programming: Rod Cutting and Matrix Chain Multiplication
Covers dynamic programming techniques for solving the rod cutting and matrix chain multiplication problems.
Linear Programming: Weighted Bipartite Matching
Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.
Optimization Algorithms: Greedy Approach
Explores optimization problems, greedy algorithms, and the Cashier's Algorithm for finding the least total number of coins.
Trade-offs in Data and Time
Explores trade-offs between data and time in computational problems, emphasizing diminishing returns and continuous trade-offs.
Dynamic Programming: Fibonacci Numbers
Explores dynamic programming through Fibonacci numbers, memoization, and rod cutting applications.
Primal-dual optimization: Theory and Computation
Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.
Optimization Problems: Greedy Algorithms
Explores optimization problems and greedy algorithms to find the best solutions efficiently.
Support Vector Machines: SVM Basics
Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.
Optimisation Algorithms: Greedy Approach
Explores optimization problems solved with greedy algorithms and proves the optimality of the Cashier's Algorithm for U.S. coins.
Microeconomic Consumer Theory
MOOC: Introduction to Discrete Choice Models
Explores microeconomic consumer theory, utility maximization, optimization problems, and decision-making models.
Asset Pricing and Hedging in Complete Markets
Covers asset pricing, hedging, American claims, stopping times, and dynamic programming in finance.
Duality: Duality in Linear Optimization
MOOC: Optimization: principles and algorithms - Linear optimization
Covers the concept of linear optimization and the duality relationship between primal and dual problems.
Dynamic Programming: Fibonacci Numbers
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Covers dynamic programming with a focus on Fibonacci numbers and the rod cutting problem.
Dynamic Programming: Rod Cutting and Matrix Chain Multiplication
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Introduces dynamic programming with a focus on rod cutting and matrix chain multiplication.
Semi-Definite Programming
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Covers semi-definite programming and optimization over positive semidefinite cones.
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