algorithms/docs/learning-plan.md

Algorithm Learning Plan

Overview

This plan provides a structured approach to learning algorithms from basic to advanced levels, organized by classes of algorithmic tasks with progressive difficulty within each class.

Learning Classes (Simple to Complex)

Phase 1: Foundations (Weeks 1-4)

Focus: Basic data structures and simple patterns

Class 1: Array & Sequence Manipulation

Concept: Basic array operations and linear patterns Existing Algorithms:

• twoSum - Two pointers pattern
• maxArea - Two pointers with area calculation
• sortedSquares - Array transformation with two pointers
• intersection - Array intersection (sorted)
• maxSum - Maximum subarray (Kadane's algorithm)
• missingNumber - Mathematical array analysis
• binarySearch - Classic search algorithm
• toGetClosestPoint - Distance-based search

Progression:

1. Week 1: Basic array traversal and simple patterns

   • Linear search, basic array operations
   • Simple frequency counting
   • Array transformation basics

2. Week 2: Two pointers fundamentals

   • Sorted array operations
   • Basic two sum variations
   • Container problems (max area)

3. Week 3: Binary search mastery

   • Classic binary search
   • Rotated array search
   • Search space problems

4. Week 4: Prefix sums and basic patterns

   • Range queries
   • Subarray problems
   • Mathematical analysis

Class 2: String Manipulation

Concept: String processing and pattern matching Existing Algorithms:

• isIsomorphic - Character mapping patterns
• isPalindrome - String reversal and comparison
• backspaceCompare - Stack-based string processing
• compress - String compression algorithms

Progression:

1. Week 5: Basic string operations

   • Character counting
   • Simple pattern matching
   • String manipulation basics

2. Week 6: Advanced string patterns

   • Isomorphic strings
   • Palindrome variations
   • Stack-based processing

Phase 2: Intermediate Patterns (Weeks 5-8)

Focus: Common algorithmic patterns and data structures

Class 3: Hash Tables & Dictionary Patterns

Concept: Hash-based solutions and frequency counting Future Topics:

• Frequency counting
• Two-sum with hash maps
• Grouping problems
• Cache implementations

Progression:

1. Week 7: Hash map fundamentals

   • Basic frequency counting
   • Lookup optimization
   • Two-sum hash approach

2. Week 8: Advanced hash patterns

   • Grouping and categorization
   • Multiple data structure combinations
   • LRU cache patterns

Class 4: Data Structure Validation

Concept: Validating complex data structures Existing Algorithms:

• isValidSudoku - Grid validation with sets
• isDevided11 - Mathematical validation
• buildTonalnost - Structure building

Progression:

1. Week 9: Grid and matrix validation

   • Sudoku solving patterns
   • Matrix traversal
   • Constraint satisfaction

2. Week 10: Mathematical and structural validation

   • Number theory applications
   • Tree/graph validation
   • Complex constraint problems

Phase 3: Advanced Patterns (Weeks 9-12)

Focus: More complex algorithms and optimization

Class 5: Dynamic Programming Basics

Concept: Optimal substructure and memoization Future Topics:

• Fibonacci patterns
• Knapsack problems
• Longest common subsequence
• Pathfinding in grids

Progression:

1. Week 11: 1D Dynamic Programming

   • Fibonacci sequence
   • Climbing stairs
   • House robber problem

2. Week 12: 2D Dynamic Programming

   • Longest common subsequence
   • Grid path problems
   • Interval DP

Phase 4: Advanced Topics (Weeks 13-16)

Focus: Complex algorithms and real-world applications

Class 6: Graph Algorithms

Future Topics:

• BFS and DFS traversal
• Connected components
• Shortest path algorithms
• Topological sorting

Class 7: Tree Algorithms

Future Topics:

• Binary tree operations
• Tree traversal patterns
• BST operations
• Tree balancing concepts

Weekly Structure

Daily Practice Routine (30-60 minutes/day)

Day 1-2: Learn new concept + 1-2 basic problems
Day 3-4: Medium difficulty problems with concept
Day 5: Review + advanced problem
Day 6-7: Mixed practice + pattern recognition

Weekly Progress Tracking

• Monday: New concept introduction
• Tuesday: Basic practice (2 problems)
• Wednesday: Medium practice (2 problems)
• Thursday: Advanced practice (1 problem)
• Friday: Review and consolidation
• Saturday: Mixed practice (2-3 problems)
• Sunday: Rest or catch-up

Problem Categories Mapping

Category 1: Array & String (Beginner)

• Pattern: Two Pointers, Sliding Window
• Complexity: O(n), O(n²)
• Problems: twoSum, maxArea, sortedSquares, isPalindrome
• LeetCode Practice: Easy level (1-2 per day)

Category 2: Hash Tables (Beginner-Intermediate)

• Pattern: Frequency Counting, Lookup Optimization
• Complexity: O(n) average, O(n²) worst
• Problems: TwoSum variations, frequency counting
• LeetCode Practice: Easy-Medium (1-2 per day)

Category 3: Binary Search (Intermediate)

• Pattern: Divide and Conquer, Search Space
• Complexity: O(log n)
• Problems: BinarySearch, rotated array search
• LeetCode Practice: Medium (1 per day)

Category 4: Dynamic Programming (Advanced)

• Pattern: Optimal Substructure, Memoization
• Complexity: O(n²), O(n³)
• Problems: Fibonacci, knapsack, LCS
• LeetCode Practice: Medium-Hard (1 every 2 days)

Category 5: Graph & Tree (Advanced)

• Pattern: Traversal, Path Finding
• Complexity: O(V+E), O(V²)
• Problems: BFS/DFS, shortest path
• LeetCode Practice: Medium-Hard (1 every 2 days)

Progress Tracking System

Progress Dashboard

Create a docs/progress.md file to track:

# Algorithm Learning Progress

## Phase 1: Foundations (Weeks 1-4)
- [ ] Week 1: Array basics completed
- [ ] Week 2: Two pointers mastered
- [ ] Week 3: Binary search mastered
- [ ] Week 4: Prefix sums completed

## Phase 2: Intermediate Patterns (Weeks 5-8)
- [ ] Week 5: String patterns mastered
- [ ] Week 6: Advanced string patterns
- [ ] Week 7: Hash map fundamentals
- [ ] Week 8: Advanced hash patterns

## Problem Solving Stats
- Total problems solved: 0
- Easy problems: 0
- Medium problems: 0
- Hard problems: 0
- Success rate: 0%

## Skill Assessment
- Array manipulation: ⭐⭐⭐⭐⭐
- String processing: ⭐⭐⭐⭐⭐
- Binary search: ⭐⭐⭐⭐⭐
- Dynamic programming: ⭐⭐⭐⭐⭐
- Graph algorithms: ⭐⭐⭐⭐⭐

Daily Practice Log

Create docs/daily-practice.md:

# Daily Practice Log

## 2026-05-17
**Today's Focus**: Two pointers pattern
**Problems Solved**: 
1. twoSum - Easy ✅
2. maxArea - Medium ✅
**Learning Points**: 
- Two pointers work well on sorted arrays
- Time complexity: O(n), Space complexity: O(1)
**Next Steps**: Practice more two pointer variations

Documentation Structure

Phase 1: Documentation Foundation

Create docs/ directory with:

1. docs/01-foundations/

   • array-basics.md - Basic array operations
   • two-pointers.md - Two pointers patterns
   • binary-search.md - Binary search variations
   • prefix-sums.md - Prefix sum techniques

2. docs/02-strings/

   • string-basics.md - Basic string operations
   • palindrome.md - Palindrome patterns
   • isomorphic.md - Character mapping
   • stack-strings.md - Stack-based processing

3. docs/03-hash-tables/

   • hash-basics.md - Hash map fundamentals
   • frequency-counting.md - Frequency patterns
   • two-sum-hash.md - Hash-based two sum

Phase 2: Advanced Documentation

4. docs/04-dynamic-programming/

   • 1d-dp.md - One-dimensional DP
   • 2d-dp.md - Two-dimensional DP
   • knapsack.md - Knapsack problems

5. docs/05-graphs-trees/

   • bfs-dfs.md - Graph traversal
   • shortest-path.md - Shortest path algorithms
   • tree-basics.md - Tree operations

Daily Practice Integration

LeetCode Integration Strategy

1. Theme Days: Focus on specific problem types

   • Monday: Array problems
   • Tuesday: String problems
   • Wednesday: Hash table problems
   • Thursday: Binary search
   • Friday: Dynamic programming
   • Saturday: Mixed practice
   • Sunday: Review/weak areas

2. Difficulty Progression:

   • Week 1-2: Easy problems only
   • Week 3-4: Easy + Medium
   • Week 5-8: Medium focus
   • Week 9-12: Medium + Hard
   • Week 13-16: Hard focus

3. External Problem Sources:

   • LeetCode "Explore" section
   • LeetCode "Top Interview Questions"
   • HackerRank practice sets
   • Codeforces beginner problems

Practice Workflow

1. Problem Analysis: 5-10 minutes

   • Understand constraints
   • Identify patterns
   • Plan approach

2. Implementation: 20-30 minutes

   • Write clean code
   • Add comments
   • Handle edge cases

3. Review: 5-10 minutes

   • Compare with solutions
   • Learn new approaches
   • Update documentation

Assessment Metrics

Weekly Assessment

• Problems Solved: Target 7-10 problems per week
• Success Rate: Aim for 80%+ success rate
• Pattern Recognition: Can identify patterns in new problems
• Time Management: Solve medium problems in <20 minutes

Monthly Review

• Progress Tracking: Update progress dashboard
• Weak Areas: Identify and focus on difficult topics
• Strengths: Reinforce strong areas
• Next Goals: Set monthly objectives

Timeline Adjustments

Fast Track (12 weeks)

• Combine similar topics
• Focus on interview patterns
• 2-3 problems per day
• Weekly mock interviews

Comprehensive Track (24 weeks)

• Deep understanding focus
• Real-world code connections
• 1-2 problems per day
• Implementation from scratch

Flexible Adjustments

• Behind Schedule: Add 1-2 extra days per topic
• Ahead Schedule: Move to advanced topics
• Stuck: Review fundamentals, add more practice

Success Criteria

Short-term (1 month)

• Complete Phase 1: Foundations
• Solve 30+ problems
• Master basic patterns
• Document learning journey

Medium-term (3 months)

• Complete Phase 2: Intermediate Patterns
• Solve 100+ problems
• Implement major algorithms from scratch
• Build intuition for pattern recognition

Long-term (6 months)

• Complete all phases
• Solve 200+ problems
• Teach others the patterns
• Contribute to open source algorithms

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Note: This plan is flexible and should be adjusted based on individual progress, learning style, and goals. Regular self-assessment ensures the plan remains effective and engaging.