Concept

Amortized analysis

Related lectures (16)

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

Explores loop invariants, time analysis, and the Divide-and-Conquer approach with a focus on Merge Sort.

Covers the analysis of algorithms, focusing on insertion sort and computational models.

Introduces the Master Theorem for analyzing algorithm time complexity through problem subdivision and formula application.

Explores the implementation and efficiency of stacks and queues, along with an algorithmic challenge involving train order determination.

Explores algorithm complexity, big-O notation, induction, recursion, and analysis of running times, covering NP problems and complexity classes.

Covers surface stress detection in Paper 3, emphasizing specific binding and signal analysis.

Covers minimum spanning trees, disjoint-set data structures, union methods, and Prim's algorithm for finding minimum spanning trees.

Covers the Bellman-Ford and Dijkstra algorithms for finding shortest paths in graphs with different edge weights.

Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.

Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.

Covers the universal constructions of log objects using linked lists and the challenges of implementation.

Explores solving linear systems iteratively and compares different solvers based on worst-case assumptions and convergence measures.

Explores errors in sampling, correlation functions, time analysis, and blocking techniques.

Covers heaps, heapsort, heap data structure, storage in arrays, and heap property maintenance.

Introduces Insertion Sort, explaining its basics, insertion process, and correctness analysis.