This is the example :

Peer Interaction: Algorithms A For this interaction, you will create yourself a resource to help you understand how (and why) to determine time complexity of an algorithm. Your resource must provide at least: charts, calculation rules and examples. As an example, you can review the resource posted under this weeks module -> supplemental resources, "Algorithmic Time Complexity 1". This is a sample of what I would like you to create and post. Important: You must create your own resources. The above is simply an example. Notes Common Algorithms, Code Features and Big-O Runtimes Algorithm Code Feature Big-O Runtime single-line determining if a binary 0(1) math integer is even or odd Constant operations binary O(log n) search loop searching an unsorted 0(n) array Linear Quicksort O(n log n) If done in- place O(log n) two nested loops three nested loops recursive Fibonacci addition of two nxn matrices multiplication of two nxn matrices some growth rate algorithms 0(n2) quadratic 0(n3) cubic 0(2) exponential Generalized Big- Calculation Rules Coefficients are dropped Examples 0(2n) -> O(n) 0(4n log n) -> O(n log n) 0(2n) remains 0(2) Keep only the highest-order term Examples O(n3 + n2 + 1) -> O(n3) On log n + n) -> O(n log n) 0 Runtimes of blocks of code that come one after another are added Example Runtime for(int x = 0; x supplemental resources, "Algorithmic Time Complexity 1". This is a sample of what I would like you to create and post. Important: You must create your own resources. The above is simply an example. Notes Common Algorithms, Code Features and Big-O Runtimes Algorithm Code Feature Big-O Runtime single-line determining if a binary 0(1) math integer is even or odd Constant operations binary O(log n) search loop searching an unsorted 0(n) array Linear Quicksort O(n log n) If done in- place O(log n) two nested loops three nested loops recursive Fibonacci addition of two nxn matrices multiplication of two nxn matrices some growth rate algorithms 0(n2) quadratic 0(n3) cubic 0(2) exponential Generalized Big- Calculation Rules Coefficients are dropped Examples 0(2n) -> O(n) 0(4n log n) -> O(n log n) 0(2n) remains 0(2) Keep only the highest-order term Examples O(n3 + n2 + 1) -> O(n3) On log n + n) -> O(n log n) 0 Runtimes of blocks of code that come one after another are added Example Runtime for(int x = 0; x