Algorithms

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deterministic nondeterministic randomized probabilistic exact approximation online offline parallel distributed concurrent synchronous asynchronous greedy dynamic programming backtracking brute force heuristic metaheuristic evolutionary genetic swarm local search hill climbing simulated annealing tabu search ant colony neural bayesian spectral primal dual streaming cache oblivious cache aware external memory data oblivious parameterized sublinear linear logarithmic quadratic exponential recursive iterative in place

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🔥🔥 big o notation → Big O notation depends on prior understanding of what algorithms are, input size, step-cost models, and correctness reasoning introduced in the overview to formalize performance measurement.

🔥🔥 deterministic algorithm → Deterministic algorithms specialize the general notion of algorithms by requiring a single predictable outcome for each input; defining and analyzing them depends on core algorithm concepts (procedures, correctness, complexity).

🔥🔥 nondeterministic algorithm → Nondeterministic algorithms extend the basic notion of an algorithm by allowing multiple possible next steps; their definition, correctness, and complexity analyses depend on core algorithmic concepts.

🌟 randomized algorithm →

🌟 approximation algorithm →

⚡️ greedy algorithm →