Master the Analysis of Algorithms

Execution time/Running time = The time it takes for an algorithm to run on a specific machine Memory to use = The amount of memory an algorithm requires to execute Dimension of the input = The size or number of elements in the input to the algorithm Number of instructions that are executed = An approximation of the execution time for a given algorithm

T(n) = The execution time of an algorithm for a given input size Input dimension = The size or number of items to be processed by an algorithm Algorithm complexity = A measure of the efficiency of an algorithm Non-deterministic algorithm = An algorithm that can exhibit different behaviors for the same input

Deterministic problems = Problems for which an algorithm can always produce the correct output for any given input Non-deterministic problems = Problems for which an algorithm may or may not produce the correct output for a given input Efficiency problems = Problems that require an algorithm to be executed within a certain time or memory constraint Complexity problems = Problems that involve the analysis of the running time and space requirements of an algorithm

<p>Efficiency = The ability of an algorithm to use resources effectively Running time = The time it takes for an algorithm to complete its execution Memory usage = The amount of memory an algorithm requires to run Execution time = The time it takes for an algorithm to execute on a specific machine</p> Signup and view all the answers

<p>$T(n) = Number of instructions that are executed$ = The execution time of an algorithm for a given input size $T = T(n)$ = The execution time of an algorithm for a given input size $n= input dimension$ = The size or number of items to be processed by an algorithm</p> Signup and view all the answers