COMP36111 Advanced Algorithms 1 Notes | Assignment Help | Syllabus
Country: United Kingdom
University: University Of Manchester
This unit offers an advanced education in algorithms, assuming that the student is familiar with algorithms for typical computational tasks, can reason about algorithm correctness, and understands the fundamentals of calculating algorithm complexity and comparing algorithmic performance.
The course examines the variety of algorithms available for computational tasks, with a focus on the fundamental distinction between tractable tasks, which can be solved using linear or polynomial-time algorithms, and tasks that appear to be intractable, in which the only algorithms available are exponential-time in the worst-case scenario.
Three issues are addressed to illustrate the breadth of algorithmic behavior and this basic divide:
Investigating a variety of typical computing problems and algorithms: For string matching tasks and issues that may be understood in terms of graphs, we’ll look at linear and polynomial-time methods. For the latter, we’ll look at the distinction between tractable and intractable problems, demonstrating that determining the range of methods accessible for any given job is challenging.
Computing complexity measurements of algorithms and comparing tasks based on their difficulty are both covered in this article. Computational task complexity classes and reduction methods There are two types of computation: deterministic and non-deterministic. Non-deterministic polynomial-time classes and polynomial-time classes. Hardness and completeness. P and NP-complete are the basic classes. Tasks that are NP-complete.
Techniques for efficient computing for intractable problems: What should you do if you’re faced with a difficult computational problem? Approximation methods and randomized/probabilistic algorithms are among the approaches that will be explored.
Advanced Algorithms (II): A follow-up course unit is offered in the second semester. This course will look at several types of algorithms for modeling and analyzing complex systems in nature and engineering. Flocking algorithms, optimisation algorithms, and numerical algorithms’ stability and accuracy are only a few examples.
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