Solving NP-hard puzzles with the oldest trick in the book (davidkoloski.me)

Article summary

The article discusses solving NP-hard puzzles using a simple yet effective approach. It highlights the importance of optimizing the solution process and explores various techniques to improve performance. The article also touches on the challenges of solving puzzles with a large search space. The approach described in the article can be applied to various puzzle types, including those with a large number of possible solutions.

Main themes

• NP-hard puzzles
• optimization techniques
• performance improvement
• puzzle solving strategies
• programming language choice
• low-level optimizations
• bidirectional search

What commenters say

• The article's approach to solving NP-hard puzzles is effective and deserves more attention.
• The performance of the solution can be improved by optimizing the heuristic function and reducing dynamic allocations.
• Working backwards to solve puzzles can be difficult due to the increased branching factor, but it may still be a viable strategy in some cases.
• The choice of programming language can significantly impact performance, with languages like C++ generally outperforming Java and Python.
• Bidirectional search can be an effective strategy for solving puzzles, but it may not always be the best approach due to the differing branching factors for forward and backward search.
• Low-level optimizations, such as using SIMD instructions and avoiding dynamic allocations, can significantly improve the performance of puzzle solvers.
• The article's approach can be applied to various puzzle types, including those with a large number of possible solutions, but may require additional optimizations and techniques to be effective.