Optimisation Algorithms: Greedy Approach

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The Trouble with Quadratic Penalties: ALM as a Fix

Explores the challenges of quadratic penalties in optimization and the use of Augmented Lagrangian Methods (ALM) as a solution.

Agency Theory: Incentives and Contracts

Explores incentive compatibility, optimal contracts, and agency costs in aligning principal-agent interests.

Dynamic Programming: Fibonacci Numbers

Covers dynamic programming with a focus on Fibonacci numbers and the rod cutting problem.

Dynamic Programming: Rod Cutting and Matrix Chain Multiplication

Covers dynamic programming techniques for solving the rod cutting and matrix chain multiplication problems.

Trade-offs in Data and Time

Explores trade-offs between data and time in computational problems, emphasizing diminishing returns and continuous trade-offs.

Support Vector Machines: SVM Basics

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

Projection Matrices: Min-Cut and Gradient Descent

Explores projection matrices in the context of min-cut and gradient descent algorithms, emphasizing their role in optimization.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Dynamic Programming: Fibonacci Numbers

Covers dynamic programming with a focus on Fibonacci numbers and efficient calculation algorithms.