When you learn about time and space complexity, there are some misunderstandings that can cause confusion. Here are a few common myths to be aware of:
Big O is Exact: Some people think that big O notation gives a precise measure of how well an algorithm performs. The truth is, it shows the best-case scenario and doesn't consider smaller factors or the less important details.
More Complexity Means Slower Algorithms: Just because an algorithm has a higher complexity, like compared to , doesn't mean it will always be slower. For small sets of data, can actually work faster.
Space Complexity is Not Important: Many people ignore this, but if an algorithm uses too much memory, it can cause systems to crash or slow down.
By knowing these myths, you can better understand how to look at how efficient algorithms are!
When you learn about time and space complexity, there are some misunderstandings that can cause confusion. Here are a few common myths to be aware of:
Big O is Exact: Some people think that big O notation gives a precise measure of how well an algorithm performs. The truth is, it shows the best-case scenario and doesn't consider smaller factors or the less important details.
More Complexity Means Slower Algorithms: Just because an algorithm has a higher complexity, like compared to , doesn't mean it will always be slower. For small sets of data, can actually work faster.
Space Complexity is Not Important: Many people ignore this, but if an algorithm uses too much memory, it can cause systems to crash or slow down.
By knowing these myths, you can better understand how to look at how efficient algorithms are!