Skip to content

Latest commit

 

History

History
122 lines (73 loc) · 6.82 KB

runtime-complexity.md

File metadata and controls

122 lines (73 loc) · 6.82 KB
Raw

Runtime Complexity

Week 3 Keywords and Questions

(see lecture slides)

  • What is runtime complexity?
  • How do we talk about runtime complexity?
  • What are examples of each the following run times: Constant, linear, O(n^2)

Prerequisites

Motivation

While computers are lightning fast, some code can run faster than other code. When computer programs get larger and larger, a slow runtime can be noticeable to the user. Luckily, code can be written in efficient ways.

Understanding runtime complexity is important for multiple reasons:

  • It will help you write fast code
  • It is a driving force when choosing which data structures to use in real-world coding
  • You will be asked about it in technical interviews

Which companies use runtime complexity? Pinterest reported that search engine traffic and sign-ups increased by 15% when the perceived load times decreased by 40%. While there are many factors that contribute to the waiting time of large websites, any company will want their programmers to be considering runtimes when writing their code.

Google also has written extensively on the importance of runtime, and why more efficient websites lead to increased web traffic.

Objectives

Participants will be able to:

  • Understand the following runtimes: O(1), O(log n), O(n), O(n log n), O(n^2), and O(2^n)
  • Know the runtime of array and object functions
  • Determine time complexity for simple functions and algorithms

Lesson

Things to know:

Runtimes to know:

There are several common runtimes that you should understand:

  • Indexing into an array (e.g. array[7]) is O(1)

  • Looking up a key in an object (e.g. object["name"]) is O(1)

  • Sorting an array (with a fast algorithm) is O(n log n) -- this isn't simple to prove, but if you're curious you can read more about why here and here

Runtime details:

  • Big-O/runtime describes the worst case scenario runtime. For example, if you're looking at each item in a list to find a specific element, the best case scenario is if it's the first element and you find it right away! But the worst case is if you look through every single item, and the one you are looking for is the last item in the list or not in the list at all. Runtime analysis focuses on the worst-case scenario.

  • Only the largest/fastest-growing term matters. For example, if a function takes n^2 + 3n steps, the function is O(n^2), because the runtime will be dominated by the n^2 term

  • When stating the runtime complexity, drop any constants. For example, if a function takes 4n steps, we drop the 4 and call it O(n). This is because runtime complexity describes how the time of the function grows with relation to the input -- not the exact time it takes to run.

  • Runtime complexity goes by many names that are generally used interchangably. E.g:

    • Time complexity
    • Asymptotic time complexity
    • Runtime analysis
    • Big O runtime
    • Growth rate analysis
    • Computational complexity

Additional Materials and Resources

Things we'll cover later

These things are not covered in this lesson, but they are related and important to know.

  • (After Interviews! Don't rabbit hole this.) - Different data structures let you do different things quickly. So far you've learned about two data structures: arrays and objects. Later, you'll learn about more data structures, including linked lists, trees, stacks, and queues. You'll learn about the runtime complexity of doing different operations with these data structures.

  • Space complexity. Similar to time complexity, algorithms can use differing amounts of memory

  • Runtime complexity is related to (but not the same!) as the total amount of time it takes to run a piece of code. A piece of O(n^2) code could run faster than a piece of O(n) code.

Independent Practice if time permits

Runtime Complexity Exercise 1: Reading code and analyzing runtime

Read the functions in runtime1-analyzing.js. For each function, figure out:

  • What does the function do?
  • What is the input size? Examples include the size of a list, the length of a string, or the integer passed into a function. This will be "n" in Big O notation.
  • Try to figure out the runtime -- O(1), O(log n), O(n), O(n log n), O(n^2), or O(2^n)
  • When the input size doubles, what would happen to the time it takes to run?

Runtime Complexity Exercise 2: Comparing code

Compare multiple pieces of code that do the same thing, and figure out the runtime of each one. Which solution would be fastest for large input sizes? runtime2-comparisions.js

Runtime Complexity Exercise 3: Solving problems and writing code

How would you solve these problems runtime3-solving.md? Can you think of an O(n^2), O(n log n), O(n) solution?

Advanced Extension

  • Try to implement the problems in runtime3-solving.md. Run your solutions on multiple input sizes. Does it match your expectations?

Check for Understanding

  • How important is it to understand runtime complexity?

  • What is Big-O/runtime?

  • What others names to Runtime complexity that are generally used interchangeably?

-Make a cheat sheet about runtime complexity. For O(1), O(log(n)), O(n), and O(nlogn) and O(n^2), give an example of 1-3 algorithms/operations that have this runtime.