<ul><li>Big O Notation is a method to measure how fast an algorithm is running.</li><li>Temporal Complexity determines how long an algorithm takes to execute relative to the input size.</li><li>Spatial Complexity determines how much memory is allocated to find the item we need.</li><li>Big O Notation helps in determining how scalable an algorithm is.</li><li>The execution time of an algorithm is denoted using Big O Notation.</li><li>Temporal constant with O(1) defines the operations that take a constant execution time.</li><li>Linear time with O(n) defines that execution time increases in proportion to the size of an array.</li><li>Logarithmic time with O(log n) means input size increases linearly, however, execution time increases logarithmically.</li><li>Linearithmic/quasilinear time with O(n log n) is a moderately growing time complexity that is implied while performing logarithmic operations n times.</li><li>Quadratic time O (n²) is when the execution time increases quadratically with the number of inputs. It generally happens when reading a matrix or when nested loops are present.</li></ul>

Big O Notations

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