Lecture

Understanding Complexity: Tractable Problems and NP-Complete

Related lectures (28)

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

Covers complexity classes, tractable problems, the class NP, NP-complete problems, and summarizes the concept of tractable problems.

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

Covers classical and quantum computational complexity concepts and implications.

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

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

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

Explores the theory of computability, decision problems, complexity classes, and the 'P vs. NP' conundrum.

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

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

Covers the time complexity of algorithms and includes a quiz.

Covers quantum algorithms, complexity classes, Grover's algorithm, and quantum information in computational complexity.

Examines NP problems, graph coloring, path optimization, and computational complexity distinctions in P and NP classes.

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

Introduces complexity classes, including P and NP, and explores examples of easy and hard problems.

Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.

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

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

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

Delves into oracles, certificates, and the NP complexity class, raising the fundamental P versus NP question.