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In algorithm design lab, analyzing and comparing time and space complexities is one of the most fundamental steps to evaluate the efficiency of algorithms. Time complexity refers to the computational time required by an algorithm as a function of the size of input, while space complexity refers to the amount of memory required to execute the algorithm, also measured in terms of input size. Both are expressed using asymptotic notations such as Big-O, Big-Theta, and Big-Omega, which describe __________ _____ ______ __________ _____.
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