I solved the subset sum problem in polynomial time

Hacker News

https://pastebin.com/j6bqLn8E I think I solved the subset sum problem, a NP-Complete problem. I compared the output of smaller sets to existing algorithms and the results match. Obviously, I cant compare larger sets due to the time complexity of existing algorithms. So my question is, what is the best path forward? I'll solve a few more of your sets below for more proof, although some sets/goals require more memory than my computer has. For now I wont share the details of the algorithm for obvious reasons. Some other interesting notes: - Goal=150: Set=(1...100), there are 19,378,091 subsets that sum to 150 (94 seconds to compute)

Visit product View on Hacker NewsNov 28, 2021

Share card

Actual performance

3points

5comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: computer, notes · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Go-dutchflag – An implementation of the Dutch flag problem in Golang37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Go-dutchflag – An implementation of the Dutch flag problem in Golang

Hacker News3

The AZ Problem39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The AZ Problem

Hacker News2

Someone Else's Problem39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Someone Else's Problem

Hacker News1

The farmer, wolf, goat and cabbage problem39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The farmer, wolf, goat and cabbage problem

Hacker News1

Monty Hall Problem39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Monty Hall Problem

Hacker News1

Wolf, Goat and Cabbage Problem39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Wolf, Goat and Cabbage Problem

Hacker News1

Guiderail – How I (partly) solved my procrastination problem46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Guiderail – How I (partly) solved my procrastination problem

Hacker News3

Are the Riemann Hypothesis and Navier-Stokes the Same Problem?56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Are the Riemann Hypothesis and Navier-Stokes the Same Problem?

Hacker News7

The problem with the epsilon greedy method33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The problem with the epsilon greedy method

Hacker News22

Grubl65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The "What's for dinner" problem solved!

Indie Hackerscommitment-full-time

Browse all products Analyze your launch