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CSC645 Algorithm Analysis And Design UITM Assignment Example Malaysia

This course provides students with a fundamental understanding of algorithms that can be applied in any area. It covers not only the design and analysis techniques but also demonstrates how to make efficient use of such problems through problem-solving strategies as well as coverage on different areas like data structures or programming languages.

In addition, the course will also help students develop skills in mathematical thinking. In fact, many of the algorithmic techniques are based on mathematical principles. As a result, not only will you learn how to solve algorithmic problems but also develop a strong foundation in mathematics that is essential for any field of study.

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At the end of the course, students will be able to design algorithms for solving problems of practical interest. They will also have a better understanding of the limits of algorithms and be familiar with different areas in computer science where algorithms are used. This knowledge will be essential as they move on to more advanced courses in the field.

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Assignment Task 1: Apply concept and theory of algorithm complexity.

An algorithm’s complexity is determined by two factors: the number of steps in the algorithm and how much volume it has to operate on. While there are definitions for effective or average complexity, which can be quite precise in some cases, in general it is possible to figure out an “order-of-magnitude estimate” when given either one factor (e.g., when the problem space is constant), though this estimation may not be very precise.

Algorithm complexity is a difficult topic to approach, as it is extraordinarily broad and includes both time and space complexity. Time complexity typically refers to the number of computational steps necessary for an algorithm’s execution. Space complexity typically consists of two primary factors: storage requirements and data transmission requirements. Storage requirements are the fixed requirement number of bits required to store the state information for any arbitrary point in computation, whereas data transmission requirements are variable based on how much has been computed thus far with that algorithm instance. Algorithms with lower storage or data transmission rate will require less total “work” than algorithms with higher rates or relative peers. To put myriad ideas into perspective, it is tempting to use parking lot attendants as a metaphor. The problem here is that there are too many possible comparisons (for most readers) to make.

Assignment Task 2: Demonstrate professionalism in selecting appropriate algorithm design techniques for algorithm analysis in solving real world problems.

Computer algorithms are widely used in the real world and there is a clear need for more professionals to analyze and design them. Computer algorithm design techniques typically provide formal specifications of problem complexity, cost considerations, use of data structures, correctness criteria, and so on. For algorithm analysis in solving real world problems, one must rely on their knowledge of many subjects such as topology or complex algebraic structures such as matrices. This often requires significant undergraduate coursework or graduate level expertise.

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The field is traditionally an interdisciplinary field of academic study that bridges mathematics and computer science but it is also highly specialized with its own terminology and practices which include aspects of both disciplines. It can be seen as one branch belonging to applied mathematics since it solves mathematical problems arising in computer science, and more specifically, combinatorial optimization and numerical linear algebra.

Computer algorithm design is the process of designing a step by step plan that will solve a problem using a computer. It is important to have an efficient algorithm because it can save time or money. The most well-known efficient algorithms were designed in a school of thought called algorithmics.

Assignment Task 3: Determine the complexity of the algorithm for space and time

Time complexity is the amount of time taken by an algorithm to complete its task, measured in number of steps. Space complexity is similar but it’s measured in the amount of space that the algorithm needs, for example how much computer memory is required. As both are closely related to each other space complexity helps measure the efficiency of algorithms. Time and space complexities are usually expressed as a function of input size n which divides into a constant k, meaning that the computation required increases exponentially with n without a large factor multiple.

So even if it takes many time steps eventually all memory consuming algorithms will run out of RAM and stop functioning while further computations on data can still continue. A good rule for measuring this is called Amdahl’s law  which states that the speedup in a parallel program is limited by the time needed for the sequential fraction of the program. For example, if a system has 100 locks and you need to acquire all of them, then it will require 100 step processes, but each process can be done in parallel with another one with no effect on any other process.

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