Pruning

Subtitle / Summary 216. Combination Sum III is where backtracking adds one more important constraint: not only must the sum match, but the combination length must be exactly k. That turns the problem into a clean fixed-length combination search over the bounded range 1..9. Reading time: 12-15 min Tags: backtracking, fixed length, pruning, combination search SEO keywords: Combination Sum III, fixed-length backtracking, k numbers, pruning, LeetCode 216 Meta description: Build the stable solution for LeetCode 216 from scratch by understanding exact-length backtracking, sorted pruning, and the bounded candidate set 1..9. A — Algorithm Problem Restatement Find all valid combinations of k numbers that add up to n, subject to these rules: ...

Subtitle / Summary If 39. Combination Sum teaches “reuse is allowed”, then 40. Combination Sum II teaches the next real upgrade: duplicate values exist, each number may be used at most once, and de-duplication must happen at the correct tree level. Reading time: 14-16 min Tags: backtracking, dedup, pruning, combination search SEO keywords: Combination Sum II, backtracking, same-layer dedup, pruning, LeetCode 40 Meta description: Build the stable solution for LeetCode 40 from scratch by understanding sorted pruning, use-once recursion, and the same-layer duplicate skip rule. A — Algorithm Problem Restatement Given a collection of candidate numbers candidates and a target integer target, return all unique combinations where the chosen numbers sum to target. ...

Subtitle / Summary Combination Sum is the first Hot100 backtracking problem that really mixes three ideas at once: combination-style search, a running target, and safe pruning after sorting. The point is not to jump straight to the template, but to build it step by step from the problem itself. Reading time: 14-16 min Tags: Hot100, backtracking, combination sum, pruning SEO keywords: Combination Sum, backtracking, pruning, remain, DFS Meta description: Learn LeetCode 39 by building the solution from scratch, using path, remain, repeated candidate reuse, and sorted pruning. A — Algorithm Problem Restatement Given an array of distinct integers candidates and a target integer target, return a list of all unique combinations of candidates where the chosen numbers sum to target. ...

Subtitle / Abstract Subgraph matching is one of the hardest parts of graph querying: NP-hard in theory, but not automatically “too slow” in production. Following the ACERS template, this article explains VF2 and Ullmann clearly, and focuses on what actually decides performance: candidate generation and pruning strategy. Estimated reading time: 15-20 minutes Tags: Subgraph Matching, VF2, Ullmann, Graph Database SEO keywords: Subgraph Isomorphism, VF2, Ullmann, candidate pruning, graph pattern matching Meta description: Starting from NP-hard subgraph isomorphism, this article explains VF2/Ullmann mechanics and practical pruning strategies for constrained graph-database pattern queries. Target Audience Engineers building pattern queries, rule detection, or risk-relationship mining in graph databases Developers who already know BFS/DFS/connected components and want stronger graph-matching skills Algorithm practitioners balancing explainable rule matching against performance limits Background / Motivation In graph databases, you regularly face requirements like: ...