Lecture

Computation & Algorithms I

Related lectures (24)

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Covers classical and quantum computational complexity concepts and implications.

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

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

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

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

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.

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Explores the complexity of algorithms, including big-O notation and efficiency analysis.

Covers the time complexity of algorithms and includes a quiz.

Covers the complexity analyses of algorithms and their worst-case time complexities.

Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.

Covers proofs of Big-O using witnesses and rules to analyze algorithm complexity.

Explains Big-O notation for algorithm complexity analysis through polynomial examples and growth rate identification.