• Why is looking at runtime not a reliable method of calculating time complexity?
• Not all computers are made equal( some may be stronger and therefore boost our runtime speed )
• How many background processes ran concurrently with our program that was being tested?
• We also need to ask if our code remains performant if we increase the size of the input.
• The real question we need to answering is: How does our performance scale?.

• Big O Notation is a tool for describing the efficiency of algorithms with respect to the size of the input arguments.
• Since we use mathematical functions in Big-O, there are a few big picture ideas that we’ll want to keep in mind:
• The function should be defined by the size of the input.
• Smaller Big O is better (lower time complexity)
• Big O is used to describe the worst case scenario.
• Big O is simplified to show only its most dominant mathematical term.

• We can use the following rules to simplify the our Big O functions:
• Simplify Products : If the function is a product of many terms, we drop the terms that don't depend on n.
• Simplify Sums : If the function is a sum of many terms, we drop the non-dominant terms.
• n : size of the input
• T(f) : unsimplified math function
• O(f) : simplified math function.

• First we apply the product rule to drop all constants.
• Then we apply the sum rule to select the single most dominant term.

The algorithm takes roughly the same number of steps for any input size.

In most cases our hidden base of Logarithmic time is 2, log complexity algorithm’s will typically display ‘halving’ the size of the input (like binary search!)

Linear algorithm’s will access each item of the input “once”.

Combination of linear and logarithmic behavior, we will see features from both classes.

Algorithm’s that are log-linear will use both recursion AND iteration.

C is a fixed constant.

C is now the number of recursive calls made in each stack frame.

Algorithm’s with exponential time are VERY SLOW.

Use When:

• The function is iterative and not recursive.
• The accompanying DS is usually an array.

• Create a table array based off the size of the input.
• Initialize some values in the table to ‘answer’ the trivially small subproblem.
• Iterate through the array and fill in the remaining entries.
• Your final answer is usually the last entry in the table.

Normal Recursive Fibonacci

Memoization Fibonacci 1

Memoization Fibonacci 2

Tabulated Fibonacci

• Worst Case Scenario: The term does not even exist in the array.
• Meaning: If it doesn’t exist then our for loop would run until the end therefore making our time complexity O(n).

• The inner for-loop contributes to O(n), however in a worst case scenario the while loop will need to run n times before bringing all n elements to their final resting spot.

• Bubble Sort will always use the same amount of memory regardless of n.

• The first major sorting algorithm one learns in introductory programming courses.
• Gives an intro on how to convert unsorted data into sorted data.

It’s almost never used in production code because:

• It’s not efficient
• It’s not commonly used
• There is stigma attached to it
• Bubbling Up : Term that infers that an item is in motion, moving in some direction, and has some final resting destination.
• Bubble sort, sorts an array of integers by bubbling the largest integer to the top.
• Worst Case & Best Case are always the same because it makes nested loops.
• Double for loops are polynomial time complexity or more specifically in this case Quadratic (Big O) of: O(n²)

• Our outer loop will contribute O(n) while the inner loop will contribute O(n / 2) on average. Because our loops are nested we will get O(n²);

• Selection Sort will always use the same amount of memory regardless of n.

• Selection sort organizes the smallest elements to the start of the array.

Summary of how Selection Sort should work:

1. 1.

   Set MIN to location 0
2. 2.

   Search the minimum element in the list.
3. 3.

   Swap with value at location Min
4. 4.

   Increment Min to point to next element.
5. 5.

   Repeat until list is sorted.

• Our outer loop will contribute O(n) while the inner loop will contribute O(n / 2) on average. Because our loops are nested we will get O(n²);

• Because we are creating a subArray for each element in the original input, our Space Comlexity becomes linear.

• Since our array gets split in half every single time we contribute O(log(n)). The while loop contained in our helper merge function contributes O(n) therefore our time complexity is O(nlog(n)); Space Complexity: O(n)
• We are linear O(n) time because we are creating subArrays.

• Merge sort is O(nlog(n)) time.
• We need a function for merging and a function for sorting.

Steps:

1. 1.

   If there is only one element in the list, it is already sorted; return the array.
2. 2.

   Otherwise, divide the list recursively into two halves until it can no longer be divided.
3. 3.

   Merge the smallest lists into new list in a sorted order.

• Even though the average time complexity O(nLog(n)), the worst case scenario is always quadratic.

• Our space complexity is linear O(n) because of the partition arrays we create.
• QS is another Divide and Conquer strategy.
• Some key ideas to keep in mind:
• It is easy to sort elements of an array relative to a particular target value.
• An array of 0 or 1 elements is already trivially sorted.

Recursive Solution

Min Max Solution

• Must be conducted on a sorted array.
• Binary search is logarithmic time, not exponential b/c n is cut down by two, not growing.
• Binary Search is part of Divide and Conquer.

• Works by building a larger and larger sorted region at the left-most end of the array.

Steps:

1. 1.

   If it is the first element, and it is already sorted; return 1.
2. 2.

   Pick next element.
3. 3.

   Compare with all elements in the sorted sub list
4. 4.

   Shift all the elements in the sorted sub list that is greater than the value to be sorted.
5. 5.

   Insert the value
6. 6.

   Repeat until list is sorted.