Update 102-big-omega.md (#7587)

Fixed inaccuracy with description
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 # Big-Ω Notation
-The Big Omega (Ω) notation is used in computer science to describe an algorithm's lower bound. Essentially, it provides a worst-case analysis of an algorithm's efficiency, giving us a lower limit of the performance. If we say a function f(n) is Ω(g(n)), it means that from a certain point onwards (n0 for some constant n0), the value of g(n) is a lower bound on f(n). It implies that f(n) is at least as fast as g(n) past a certain threshold. This means that the algorithm won't perform more efficiently than the Ω time complexity suggests.
+The Big Omega (Ω) notation is used in computer science to describe an algorithm's lower bound. Essentially, it provides a best-case analysis of an algorithm's efficiency, giving us a lower limit of the performance. If we say a function f(n) is Ω(g(n)), it means that from a certain point onwards (n0 for some constant n0), the value of g(n) is a lower bound on f(n). It implies that f(n) is at least as fast as g(n) past a certain threshold. This means that the algorithm won't perform more efficiently than the Ω time complexity suggests.
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	`# Big-Ω Notation`	`# Big-Ω Notation`

	The Big Omega (Ω) notation is used in computer science to describe an algorithm's lower bound. Essentially, it provides a worst-case analysis of an algorithm's efficiency, giving us a lower limit of the performance. If we say a function f(n) is Ω(g(n)), it means that from a certain point onwards (n0 for some constant n0), the value of g(n) is a lower bound on f(n). It implies that f(n) is at least as fast as g(n) past a certain threshold. This means that the algorithm won't perform more efficiently than the Ω time complexity suggests.	The Big Omega (Ω) notation is used in computer science to describe an algorithm's lower bound. Essentially, it provides a best-case analysis of an algorithm's efficiency, giving us a lower limit of the performance. If we say a function f(n) is Ω(g(n)), it means that from a certain point onwards (n0 for some constant n0), the value of g(n) is a lower bound on f(n). It implies that f(n) is at least as fast as g(n) past a certain threshold. This means that the algorithm won't perform more efficiently than the Ω time complexity suggests.