Top 50 Dynamic Programming Java Algorithms Coding Questions

Top 50 Dynamic Programming Java Algorithms Coding Questions
Published 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

Solve the top 50 Dynamic Programming Java Algorithms Questions to ace Coding Interview and Competitive Programming.

What you’ll learn

Understand what is Dynamic Programming, Recursion, and back tracking

Learn the criteria to classify a dynamic programming question using optimal substructure and overlapping subproblems

Solve top 50 handpicked dynamic programming Java algorithm questions asked in competitive programming and programming interviews

Solve each question in recursive, top-down (memoization) and bottom-up (tabulation) dynamic programming approaches

Get one step closer to competitive programming and acing coding interview

Requirements

Understanding of any basic programming language (preferably Java)

Basic understanding of time and space complexities

Description

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Introduction to Dynamic Programming – Recursive, Memoization, Tabulation

Section 2: Binomial Coefficient Problem

Lecture 3 Binomial Coefficient Problem

Section 3: Maximize the Cut Segments

Lecture 4 Maximize the Cut Segments

Section 4: Friends Pairing Problem

Lecture 5 Friends Pairing Problem

Section 5: Rod Cutting Problem

Lecture 6 Rod Cutting Problem

Section 6: Gold Mine Problem

Lecture 7 Gold Mine Problem

Section 7: Nth Catalan Number

Lecture 8 Nth Catalan Number

Section 8: Largest Sum Contiguous SubArray (Kadane’s Algorithm)

Lecture 9 Largest Sum Contiguous SubArray (Kadane’s Algorithm)

Section 9: Climbing Stairs

Lecture 10 Climbing Stairs

Section 10: Maximum Sum Increasing Subsequence

Lecture 11 Maximum Sum Increasing Subsequence

Section 11: House Robber

Lecture 12 House Robber

Section 12: Subset Sum Problem

Lecture 13 Subset Sum Problem

Section 13: Longest Common Subsequence

Lecture 14 Longest Common Subsequence

Section 14: Longest Increasing Subsequence

Lecture 15 Longest Increasing Subsequence

Section 15: Weighted Job Scheduling

Lecture 16 Weighted Job Scheduling

Section 16: Maximum Length Chain of Pairs

Lecture 17 Maximum Length Chain of Pairs

Section 17: Maximum Sum of Disjoint Pairs with Specific Difference

Lecture 18 Maximum Sum of Disjoint Pairs with Specific Difference

Section 18: Egg Dropping Problem

Lecture 19 Egg Dropping Problem

Section 19: Minimum Removals from Array to make Max – Min <= K (DP + Binary Search)

Lecture 20 Minimum Removals from Array to make Max – Min <= K (DP + Binary Search)

Section 20: Maximum Size Square Sub-Matrix with all 1s

Lecture 21 Maximum Size Square Sub-Matrix with all 1s

Section 21: Largest Area Rectangular Sub-Matrix with Equal 0’s and 1’s

Lecture 22 Largest Area Rectangular Sub-Matrix with Equal 0’s and 1’s

Section 22: Largest Rectangular Sub-Matrix whose Sum is 0

Lecture 23 Largest Rectangular Sub-Matrix whose Sum is 0

Section 23: Maximum Sum Rectangle in a 2D Matrix

Lecture 24 Maximum Sum Rectangle in a 2D Matrix

Section 24: Longest Palindromic Subsequence

Lecture 25 Longest Palindromic Subsequence

Section 25: Longest Palindromic Substring

Lecture 26 Longest Palindromic Substring

Section 26: Longest Alternating Subsequence

Lecture 27 Longest Alternating Subsequence

Section 27: Count All Palindromic Subsequence in a Given String

Lecture 28 Count All Palindromic Subsequence in a Given String

Section 28: Longest Common Substring

Lecture 29 Longest Common Substring

Section 29: Longest Subsequence such that Difference Between Adjacent is 1

Lecture 30 Longest Subsequence such that Difference Between Adjacent is 1

Section 30: Longest Repeated Subsequence

Lecture 31 Longest Repeated Subsequence

Section 31: Word Break Problem

Lecture 32 Word Break Problem

Section 32: Permutation Coefficient Problem

Lecture 33 Permutation Coefficient Problem

Section 33: Minimum Number of Jumps to Reach End

Lecture 34 Minimum Number of Jumps to Reach End

Section 35: Count Derangements

Lecture 36 Count Derangements

Section 36: Count Number of Ways to Reach a Given Score in a Game

Lecture 37 Count Number of Ways to Reach a Given Score in a Game

Section 38: Matrix Chain Multiplication

Lecture 39 Matrix Chain Multiplication

Section 39: Maximum Subsequence Sum such that no 3 Elements are Consecutive

Lecture 40 Maximum Subsequence Sum such that no 3 Elements are Consecutive

Section 40: Painting the Fence

Lecture 41 Painting the Fence

Section 41: Largest Independent Set of Nodes in a Binary Tree

Lecture 42 Largest Independent Set of Nodes in a Binary Tree

Section 42: Count Balanced Binary Trees of Height H

Lecture 43 Count Balanced Binary Trees of Height H

Section 43: Coin Change Problem

Lecture 44 Coin Change Problem

Section 44: Optimal Strategy for a Game
Lecture 45 Optimal Strategy for a Game
Section 45: Unique Paths

Lecture 46 Unique Paths

Section 46: Minimum Cost Path

Lecture 47 Minimum Cost Path

Section 47: Coin Game Winner Where Every Player has 3 Choices

Lecture 48 Coin Game Winner Where Every Player has 3 Choices

Section 48: Edit Distance

Lecture 49 Edit Distance

Section 49: 0/1 Knapsack Problem

Lecture 50 0/1 Knapsack Problem

Section 50: Find if a String is Interleaved of 2 Other Strings

Lecture 51 Find if a String is Interleaved of 2 Other Strings

Section 51: Maximum Profit by Buying and Selling a Share at most Twice

Lecture 52 Maximum Profit by Buying and Selling a Share at most Twice

Students or professionals who are preparing for software developer related coding interview,Programmers who want to get into competitive programming,Self-taught programmers who want to learn data structures and algorithms