Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Dynamic Programming for the day before your coding interview

Dynamic Programming for the day before your coding interview

Kiao, Ue
Chatterjee, Aditya

Paperback

Series: Day Before Coding Interview [Dci]: Coding Interview Patterns, Book 7

General ComputersProgramming

ISBN13: 9798640923216
Publisher: Independently Published
Published: Apr 28 2020
Pages: 78
Weight: 0.45
Height: 0.16 Width: 8.50 Depth: 11.00
Language: English
Master DYNAMIC PROGRAMMING in 2 hours. Includes DP CHEATSHEET (goldmine).

Dynamic Programming is a fundamental algorithmic technique which is behind solving some of the toughest computing problems. In this book Dynamic Programming for the day before your coding interview, we have covered some Dynamic Programming problems which will give you the general idea of formulating a Dynamic Programming solution and practice on applying it on a variety of Coding Interview problems.

The first chapter is the Dynamic Programming cheatsheet. This is a goldmine. This will help you get the core DP idea of over 50 problems within 30 minutes. You should revisit and revise this cheatsheet every 2 months.

Some of the problems we have covered are:

  • Permutation coefficient: This is a basic problem but is significant in understanding the idea behind Dynamic Programming.
  • Longest Common Substring: This is an important problem as we see how we can apply Dynamic Programming in string problems.
  • XOR value: This is another significant problem as we are applying Dynamic Programming on a Number Theory problem more specifically problem involving subset generation.
  • K edges: In line with our previous problems, in this problem, we have applied Dynamic Programming in a graph-based problem. This problem shows a problem where Dynamic Programming is not the most efficient solution but is in the right path.

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