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Courses tagged 'Computer Science, Elective'
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Computer Architecture
Vector processing is an efficient technique for handling large datasets by performing operations on multiple data elements simultaneously. This chapter explores SIMD, which enhances parallel computing capabilities in CPUs and GPUs, enabling faster processing for various applications such as graphics rendering and machine learning. Furthermore, advancements in SIMD architectures and the rise of General-Purpose GPUs (GPGPUs) have transformed computation across sectors by efficiently managing vast amounts of parallelizable tasks.
Pavan
Design & Analysis of Algorithms - Vol 2
The chapter discusses memoization and dynamic programming as strategies to optimize recursive computations, particularly in the context of defining functions like Fibonacci. Memoization prevents redundant calculations by storing previously computed results, while dynamic programming eliminates recursion by systematically filling in values based on identified dependencies. Through these strategies, computational efficiency improves significantly, addressing the challenges of overlapping subproblems in recursive definitions.
Abraham
Design & Analysis of Algorithms - Vol 1
The chapter explores the Bellman-Ford algorithm as a method for finding the shortest paths in graphs, especially those containing negative edge weights. It discusses the limitations and assumptions of Dijkstra's algorithm and contrasts them with the reassurances provided by Bellman-Ford when negative cycles are not present. The emphasis is placed on the determination of shortest paths through systematic updates rather than greedy choices.
Abraham
Design & Analysis of Algorithms - Vol 3
The chapter delves into the concept of intractability in algorithms, emphasizing the distinction between generating and checking solutions. It highlights important problems such as Boolean satisfiability and the traveling salesman problem, noting that while finding efficient solutions may be difficult or impossible, checking their validity often is not. The chapter concludes by illustrating the relationship between various computational problems and their checking algorithms.
Abraham
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