Lecture

Complexity of Algorithms

Related lectures (21)

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

Explores advanced big-O facts for powers, logarithms, factorials, and function combinations.

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

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

Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.

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

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

Analyzes the growth of functions to understand algorithm efficiency and uses Big-O notation for characterization.

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

Analyzes the growth of functions to understand algorithm complexity and efficiency.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

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

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

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

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.