Tag: algorithm efficiency
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Big Thetha
Big Θ (Theta) notation is a mathematical concept used to describe the tight bound of an algorithm’s performance. Unlike Big O, which focuses on the worst-case scenario, or Big Ω, which captures the best-case scenario, Big Θ provides a precise measure of the algorithm’s growth rate by considering both upper and lower bounds. It essentially…
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Big Omega
Big Ω (Omega) notation is a mathematical concept used to describe the best-case performance of an algorithm. It provides a lower bound on the running time or space required by an algorithm as a function of the input size . In simpler terms, Big Ω defines the minimum time an algorithm will take, regardless of…
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Big o notation
Big O Notation is a mathematical concept widely used in computer science to describe the efficiency and scalability of algorithms. It provides a framework to evaluate how the runtime or space requirements of an algorithm grow relative to the size of the input data. By abstracting away hardware and implementation specifics, Big O focuses on…
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Time Complexity (Code Time Optimization)
Time complexity is a measure in computer science that evaluates the amount of time an algorithm takes to complete based on the size of its input. It describes the growth rate of an algorithm’s running time as the input data grows, providing insight into the efficiency and scalability of the algorithm. Time complexity is crucial…
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Big – O – Notation (time & space complexity)
The Big-O notation is a mathematical concept used in computer science to describe the efficiency of an algorithm based on its time or space complexity as the input size grows. It provides a way to measure the upper limit of an algorithm’s performance, helping developers estimate scalability and potential bottlenecks. Key Concepts of Big-O Notation…