Complexity of AlgorithmsExplores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Elements of Computational ComplexityIntroduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Introduction to ComplexityIntroduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.
Introduction to ComplexityIntroduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.
Complexity Classes: P and NPExplores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Learning from Probabilistic ModelsDelves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.
Optimization AlgorithmsCovers optimization algorithms, convergence properties, and time complexity of sequences and functions.
Dynamic Programming: KnapsackExplores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
