Complexity of Algorithms: Big-O Notation

This lecture covers the complexity of algorithms, focusing on big-O notation, induction, recursion, and the analysis of algorithm running times. It discusses common complexity classes, such as constant, logarithmic, linear, polynomial, and exponential. The instructor explains the impact of complexity on computer time and introduces tractable problems, NP class, NP-complete problems, and 3-SAT problem. Examples like the Knapsack Decision Problem are used to illustrate NP problems and NP-complete problems. The session concludes with a summary of tractable problems and their classification in the P class.