Lectures related to Algorithmic paradigm

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Maximum Subarray Problem

Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.

Dynamic Programming: Fibonacci Numbers

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

Algorithm Design: Divide and Conquer

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Problem-solving Strategies 2: Recursion

Explores problem-solving strategies like recursion and divide and conquer methods, with examples such as the Towers of Hanoi.

Dynamic Programming: Fibonacci Numbers

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

Dynamic Programming: Rod Cutting and Change Making

Explores dynamic programming through rod cutting and change making optimization problems.

Differential Privacy: Privacy Guarantees and Mechanisms

Covers differential privacy, global noise sensitivity, Laplace Mechanism, and privacy-accuracy tradeoff in algorithm design.

Dynamic Programming: Rod Cutting and Matrix Chain Multiplication

Introduces dynamic programming with a focus on rod cutting and matrix chain multiplication.

Greedy Algorithms & Matroids

Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.

Algorithms for Composite Optimization

Explores algorithms for composite optimization, including proximal operators and gradient methods, with examples and theoretical bounds.

Complexity & Induction: Algorithms & Proofs

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Problem Solving Strategies: General Overview

Presents methods for problem-solving, emphasizing 'Divide and Conquer', recursion, and dynamic programming.

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: Rod Cutting and Matrix Chain Multiplication

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

State Space Models: Expressivity of Transformers

Covers state space models and the expressivity of transformers in sequence copying tasks.

Partition Functions and Models

Explores partition functions in spin systems, models like Ising and Potts, computational challenges, and the Lee-Yang Theorem.

Branch & Bound Algorithm: LP Based Approach

Explores the LP-based Branch & Bound algorithm for finding optimal solutions.

Algorithm Design: Time Complexity Analysis

Introduces algorithm design and time complexity analysis using examples and notations like n² and O(n).

Modeling Prisoner's Dilemma: Naive vs. Optimal

Explores the modeling of the '100 prisoners' problem and compares naive and optimal approaches.