Concept

L (complexity)

Related lectures (30)

Distinct Elements: Count and Hash Functions

Covers counting distinct elements using hash functions and the median trick.

Covers challenges in bit-precise reasoning, including SMT-COMP results, AIG, bit-blasting, Tseitin transformation, and complexity classes.

Complexity Classes: Introduction and Examples

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

Algorithmes: introduction

Covers the basics of algorithms, problem-solving, and efficient resolution methods.

Promise Constraint Satisfaction and Width

Covers Promise Constraint Satisfaction Problems complexity, width, graph coloring, polymorphisms, and algorithms.

Search Algorithms: Linear vs Logarithmic

Illustrates the efficiency of logarithmic search algorithms over linear ones, emphasizing the importance of data modeling and the trade-off between sorting and searching costs.

Search Algorithms: Dichotomy Search

Explores dichotomy search algorithms, analyzing complexity and implementation details for efficient searching in sorted lists.

Polar Coding for Binary Input Channels

Covers polar coding for binary input channels with low computational complexity and explicit identification.

Stacks and Queues: Implementation and Efficiency

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

Computational Complexity

Covers the basics of computational complexity, including big O notation and complexity classes.

Elements of Computational Complexity

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

Matrix Computations: Complexity and Algorithms

Explores matrix computation complexity, known algorithms, and structured solvers' impact.

Complexity of Algorithms

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

Introduction to Complexity

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

Introduction to Complexity

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

Elements of computational complexity

Covers classical and quantum computational complexity concepts and implications.

Theory of Computability: Solvability and Complexity

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

Theory of Computation: Countability and Undecidable Problems

Explores countability and undecidable problems in the theory of computation.

Complexity of Algorithms

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

Elements of Computational Complexity

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