Core Principles of Computation: Complexity, Automata, and Algorithms
1.) What is computational complexity theory, and why is it important? It studies how efficiently problems can be solved using algorithms. 2.) Explain the difference between time complexity and space complexity. Time complexity measures how the runtime of an algorithm grows with input size, while space complexity measures how much memory an algorithm uses as input size grows. 3.) What are P and NP classes in complexity theory? P contains problems that can be solved quickly (in polynomial time), while NP contains problems whose solutions can be verified quickly. 4.) What does it mean when a problem is NP-complete? It means the problem is one of the hardest in NP; solving one NP-complete problem quickly means all NP problems can be solved quickly.... Continue reading "Core Principles of Computation: Complexity, Automata, and Algorithms" »
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