dp-pattern-library

name	dp-pattern-library
description	Maintain and match against a library of classic dynamic programming patterns. Provides pattern matching, template code generation, variant detection, and problem-to-pattern mapping for DP problems.
allowed-tools	Bash, Read, Write, Grep, Glob, WebSearch
metadata	{"author":"babysitter-sdk","version":"1.0","category":"algorithms-optimization","skill-id":"SK-ALGO-012","priority":"high"}

dp-pattern-library

A specialized skill for dynamic programming pattern recognition, matching problems to known DP patterns, generating template code, and providing optimization guidance for DP solutions.

Purpose

Assist with dynamic programming by:

• Matching problems to 50+ classic DP patterns
• Generating template code for matched patterns
• Detecting problem variants (knapsack variants, LCS variants, etc.)
• Providing state design recommendations
• Suggesting optimization techniques

Capabilities

Core Features

1. Pattern Recognition

   • Analyze problem statement for DP indicators
   • Match to known pattern categories
   • Identify problem variants and transformations
   • Suggest state representation

2. Pattern Categories

   • Linear DP (1D array)
   • Grid/Matrix DP (2D paths)
   • String DP (LCS, edit distance)
   • Interval DP (ranges, parenthesization)
   • Tree DP (subtree problems)
   • Bitmask DP (subset enumeration)
   • Digit DP (number counting)
   • Knapsack variants
   • DP with state machine

3. Code Generation

   • Template code for recognized patterns
   • Multiple language support (Python, C++, Java)
   • Comments explaining state and transitions
   • Space-optimized variants

4. Optimization Guidance

   • Rolling array technique
   • Convex hull trick
   • Divide and conquer optimization
   • Monotonic queue/stack optimization
   • Knuth optimization

Pattern Library

Linear DP Patterns

Pattern	State	Transition	Example Problems
Fibonacci	dp[i] = answer for position i	dp[i] = dp[i-1] + dp[i-2]	Climbing Stairs, House Robber
Min/Max Path	dp[i] = best answer ending at i	dp[i] = opt(dp[j]) + cost(j,i)	Minimum Path Sum
Counting	dp[i] = ways to reach state i	dp[i] = sum(dp[j])	Unique Paths, Decode Ways
LIS	dp[i] = LIS ending at i	dp[i] = max(dp[j]) + 1 where j < i, a[j] < a[i]	Longest Increasing Subsequence

String DP Patterns

Pattern	State	Example Problems
Edit Distance	dp[i][j] = distance for s1[0..i], s2[0..j]	Edit Distance, One Edit Distance
LCS	dp[i][j] = LCS of s1[0..i], s2[0..j]	Longest Common Subsequence
Palindrome	dp[i][j] = is s[i..j] palindrome	Longest Palindromic Substring
Regex Match	dp[i][j] = s[0..i] matches p[0..j]	Regular Expression Matching

Knapsack Patterns

Variant	State	Transition
0/1 Knapsack	dp[i][w] = max value with items 0..i, capacity w	dp[i][w] = max(dp[i-1][w], dp[i-1][w-wt[i]] + val[i])
Unbounded	dp[w] = max value with capacity w	dp[w] = max(dp[w], dp[w-wt[i]] + val[i])
Bounded	dp[i][w] = max value with limited items	Use binary representation or deque
Subset Sum	dp[i][s] = can reach sum s with items 0..i	dp[i][s] = dp[i-1][s] or dp[i-1][s-a[i]]

Grid DP Patterns

Pattern	State	Example Problems
Path Count	dp[i][j] = ways to reach (i,j)	Unique Paths, Unique Paths II
Path Min/Max	dp[i][j] = best path to (i,j)	Minimum Path Sum
Multi-path	dp[i][j][k][l] = two paths simultaneously	Cherry Pickup

Interval DP Patterns

Pattern	State	Example Problems
MCM	dp[i][j] = cost for range [i,j]	Matrix Chain Multiplication
Burst	dp[i][j] = max coins from balloons[i..j]	Burst Balloons
Merge	dp[i][j] = cost to merge range [i,j]	Minimum Cost to Merge Stones

Tree DP Patterns

Pattern	State	Example Problems
Subtree	dp[v] = answer for subtree rooted at v	Binary Tree Maximum Path Sum
Rerooting	dp[v] = answer when v is root	Sum of Distances in Tree
Parent-Child	dp[v][0/1] = answer with constraint	House Robber III

Bitmask DP Patterns

Pattern	State	Example Problems
TSP	dp[mask][last] = min cost visiting mask cities ending at last	Traveling Salesman Problem
Assignment	dp[mask] = min cost assigning tasks to subset	Task Assignment
SOS	dp[mask] = sum over subsets	Subset Sum over Subsets

Usage

Pattern Matching

# Match problem to DP pattern
dp-pattern-library match --problem "Given an array of integers, find the longest increasing subsequence"

# Output:
# Pattern: Linear DP - Longest Increasing Subsequence (LIS)
# State: dp[i] = length of LIS ending at index i
# Transition: dp[i] = max(dp[j] + 1) for all j < i where arr[j] < arr[i]
# Time: O(n^2) naive, O(n log n) with binary search
# Space: O(n)

Template Generation

# Generate template code
dp-pattern-library template --pattern "lis" --language python

# Output:
def lengthOfLIS(nums):
    if not nums:
        return 0

    n = len(nums)
    # dp[i] = length of LIS ending at index i
    dp = [1] * n

    for i in range(1, n):
        for j in range(i):
            if nums[j] < nums[i]:
                dp[i] = max(dp[i], dp[j] + 1)

    return max(dp)

Optimization Suggestions

# Get optimization recommendations
dp-pattern-library optimize --pattern "lis"

# Output:
# Current: O(n^2) time, O(n) space
# Optimizations:
# 1. Binary Search: O(n log n) time
#    - Maintain sorted list of smallest tail elements
#    - Binary search for insertion point
# 2. Segment Tree: O(n log n) time
#    - For coordinate compression + range max query

Output Schema

{
  "match": {
    "pattern": "Linear DP - LIS",
    "confidence": 0.95,
    "category": "linear",
    "variants": ["LIS", "LDS", "LNDS"]
  },
  "state": {
    "description": "dp[i] = length of LIS ending at index i",
    "dimensions": 1,
    "meaning": "LIS length ending at position i"
  },
  "transition": {
    "formula": "dp[i] = max(dp[j] + 1) for j < i, arr[j] < arr[i]",
    "baseCase": "dp[i] = 1 for all i",
    "order": "left to right"
  
   
     
     
     
       
       
    
  
   
     
     
     
  
   
    
    
    
    
  

Integration with Processes

This skill enhances:

• dp-pattern-matching - Core pattern matching workflow
• dp-state-optimization - State space optimization
• dp-transition-derivation - Deriving transitions
• leetcode-problem-solving - DP problem identification
• classic-dp-library - Building a personal DP library

Pattern Recognition Indicators

Indicator	Likely Pattern
"maximum/minimum" + "subarray/subsequence"	Linear DP
"number of ways"	Counting DP
"can reach/achieve"	Boolean DP
"edit/transform string"	String DP
"merge/combine intervals"	Interval DP
"tree/subtree"	Tree DP
"select subset" + small n	Bitmask DP
"count numbers with property"	Digit DP
"items + capacity"	Knapsack

References

• Dynamic Programming Patterns
• DP Visualization Tools
• LeetCode DP Patterns
• CP Algorithms - DP
• CSES DP Section

Error Handling

Error	Cause	Resolution
NO_PATTERN_MATCH	Problem doesn't fit known patterns	Consider greedy or other approaches
AMBIGUOUS_MATCH	Multiple patterns could apply	Provide more problem details
COMPLEX_STATE	State too complex for templates	Manual state design needed

Best Practices

1. Start with brute force - Understand recurrence before optimizing
2. Draw state diagram - Visualize transitions
3. Verify base cases - Most DP bugs are in base cases
4. Check state uniqueness - Each state should be uniquely defined
5. Consider space optimization - Often can reduce dimension
6. Test with small inputs - Trace through by hand