Foundations of Algorithmic Analysis

Analyze the time and space complexity of iterative and recursive algorithms using Big O, Omega, and Theta notation.

Differentiate between best, average, and worst-case complexities for common algorithms.

Apply the master theorem to analyze the complexity of divide-and-conquer recurrences.

Apply amortized analysis to understand the average performance of data structure operations over a sequence.

Solve simple recursive problems with proper base cases to build foundational recursion skills.

Implement binary search on sorted arrays and its variations for problems like search in rotated arrays.

Compare time-space trade-offs in algorithm design decisions.

Apply bit manipulation techniques including setting, clearing, toggling bits, and using XOR properties.