Such algorithms are often quite efficient and provide useful bounds on the optimal solutions. The learning will be supported by instructor provided notes, readings from textbooks and assignments.

It covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) .

Introduction to theory of algorithms and basics of Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

Static program analysis is a cornerstone in verifying, optimising, and debugging modern software systems. By evaluating code without executing it, these techniques enable early detection of ...

Introduction to theory of algorithms and basics of Python programming. The Euclidean algorithm. Time complexity of algorithms. Asymptotic notation. Heaps. Sorting. Recursive algorithms. Stacks and ...