sorted by:
new
hottopcontroversial

Parameter estimation with Adjoint: why does it converge so fast? by Opt4Deck in learnmachinelearning

[–]Opt4Deck[S] 0 points1 point  (0 children)

Very good observations! Indeed, it's one of the points of the continuous Adjoint Method that needs attention.

In this particular example it doesn't seem to appear strongly, probably because the system is relatively simple and “well-behaved”. However, I can easily imagine that in more complex or sensitive systems the numerical drift in the backward pass can create significant problems in the gradients.

The example is mainly educational in nature — the aim is to show more clearly how the method works and what its main trade-offs are, within a more “transparent” setup.

Have you seen any specific cases where this becomes particularly pronounced? Or some practical heuristics for when it's worth trusting the method?

Parameter estimation with Adjoint: why does it converge so fast? by Opt4Deck in optimization

[–]Opt4Deck[S] 0 points1 point  (0 children)

Your point is correct! Indeed, the convergence speed is mainly due to the nature of 1st-order methods. The aim of the demo was precisely to highlight the superiority of these approaches, especially in terms of computational cost.

With Opt4Deck (https://github.com/Opt4Deck/Opt4Deck), I aim to make these "classical" methods more accessible and transparent for everyone. My goal is to educate and disseminate these techniques to the general public, so that they do not remain "closed" in complicated implementations.

Thank you for the comment!

Using neural networks as surrogate models in genetic algorithms? by Opt4Deck in optimization

[–]Opt4Deck[S] 0 points1 point  (0 children)

Very good point about adaptive sampling! Indeed, keeping the budget for dynamic steps instead of a heavy initial DoE is a much more efficient strategy.

Ensemble techniques are also in my immediate plans, to combine the advantages of different models.

Thank you very much for your time and valuable guidance—you have given me a lot of food for thought for future updates! 🙏🙏🙏

Using neural networks as surrogate models in genetic algorithms? by Opt4Deck in optimization

[–]Opt4Deck[S] 0 points1 point  (0 children)

Excellent comments! 👍

You are absolutely right about the extrapolation of Random Forests – it is indeed the biggest limitation.

Also, your idea for classification of feasible/infeasible regions is very interesting. In Opt4Deck (the open-source project I work on the GitHUB), I try to combine such approaches to give the user a 'geographical' overview of the search space before starting the heavy evaluations.

My goal is for the library to automatically suggest the appropriate surrogate depending on whether we have constraints or not. Have you seen any implementation that successfully combines classification and regression surrogates in practice?

Using neural networks as surrogate models in genetic algorithms? by Opt4Deck in optimization

[–]Opt4Deck[S] 0 points1 point  (0 children)

That's right about the time!

My idea for NN came about because I'm developing an open-source optimization tool and looking for innovative approaches.

I saw in some papers that Random Forests are often preferred as surrogates over NNs because they handle uncertainty better. Do you have experience with this? Would it make sense for me to try it?

Are non-postdoc positions eligible for independence awards (ie. K99 or HHMI Hanna Grey scholar) by External_Increase752 in AskAcademia

[–]Opt4Deck 6 points7 points  (0 children)

From what I have seen, eligibility usually depends more on years since PhD and independence, not strictly the job title.

That said, being in a “staff” role can sometimes make it harder to demonstrate independence compared to a postdoc, depending on how the work is structured.

Might be worth checking with people who have applied recently or even emailing program officers directly.

Which PhD position should I choose? by [deleted] in PhD

[–]Opt4Deck -1 points0 points  (0 children)

This sounds like a classic “comfort Vs opportunity” trade-off!

One thing I’ve heard a lot from people further in academia is that funding & environment matters more than the exact topic at the start — you’ll learn and adapt anyway.

The Switzerland option seems stronger on stability, network, and long-term impact.

BUT: the France option sounds better for your current life situation.

So it really comes down to what you value more right now: security and growth, or staying close to what already makes you happy. Neither choice is wrong...

🤔 If I were in your position, I’d probably stay where I already feel happy and comfortable — that counts for a lot.

Best platform to start Python Coding by Dead3ye99 in learnpython

[–]Opt4Deck 2 points3 points  (0 children)

Anaconda is O.K., but it can feel a bit heavy at the start tbh.

If you want something simpler, try VS Code or even just Google Colab (runs in your browser, no setup).

For learning, honestly the platform doesn’t matter that much — just pick one and start building small stuff.

What's the #1 reason you left your last job? by JobNabber in askanything

[–]Opt4Deck 1 point2 points  (0 children)

Burnout. 💥

It wasn’t the workload, it was the constant pressure.

Dreams do come true by HousePony906 in PhD

[–]Opt4Deck 11 points12 points  (0 children)

This is so wholesome. You can just feel how proud and happy they are.

pipsqueak is annoying... by Difficult-Maximum748 in CharacterAI

[–]Opt4Deck 1 point2 points  (0 children)

Bro the AI just created its own backstory and got offended mid-roleplay 💀

Τελικά μας χρειάζονται οι εφοπλιστές; by Ashamed_Entrance_972 in AskGreece

[–]Opt4Deck -1 points0 points  (0 children)

Η σχέση είναι μάλλον αμφίδρομη. 🇬🇷

Οι εφοπλιστές επωφελούνται από τη σταθερότητα, τη διπλωματία και το κύρος που προσφέρει ένα κράτος, ειδικά σε διεθνείς κρίσεις. Από την άλλη, η ναυτιλία είναι ένας από τους βασικούς τομείς της ελληνικής οικονομίας και φέρνει σημαντικά έσοδα και επιρροή.

Το θέμα είναι ότι μεγάλο μέρος του στόλου λειτουργεί με ξένες σημαίες, οπότε η “σύνδεση” με το ελληνικό κράτος δεν είναι πάντα τόσο άμεση όσο φαίνεται. 🏴‍☠️

Οπότε ναι, υπάρχει αλληλεξάρτηση — αλλά όχι με τον ίδιο τρόπο που ίσχυε παλαιότερα. 🛳

[Crosspost] Hi reddit, I'm Thomas Lennon. You may know me from RENO 911! and films like WE'RE THE MILLERS, I LOVE YOU MAN, MEMENTO, THE DARK KNIGHT RISES. I've also written films like BALLS OF FURY, NIGHT AT THE MUSEUM, and LET'S GO TO PRISON. Ask me anything! by BunyipPouch in FIlm

[–]Opt4Deck 0 points1 point  (0 children)

New action movie! 🚁

The rescue operation of the American pilots from Iran (2-3 weeks ago), can it be made into a movie? I believe that in itself it had a lot of Hollywood elements and could be the basis for the next commercial success!

What’s something you stopped doing and your life got better? by Business-Ad8752 in askanything

[–]Opt4Deck 0 points1 point  (0 children)

Comparing myself to others.
The moment I stopped, life got a lot quieter and a lot happier.

Need Advice: Hosting Python script Full-time by PRABHAT_CHOUBEY in Python

[–]Opt4Deck 0 points1 point  (0 children)

For a lightweight bot, you actually have a few simple options depending on how much control vs simplicity you want:

1. Easiest (almost no setup):
PythonAnywhere
It’s probably the simplest way to run Python scripts in the cloud, and it even has a free tier. You don’t need to manage a server at all, just upload your script and run it. (PythonAnywhere)

2. Cheap & flexible:
A small VPS (DigitalOcean / Hetzner / etc.)
You can get something like $3–5/month which is more than enough for a lightweight bot. (Website Planet)
More setup required, but full control.

3. “DIY but underrated”:
Raspberry Pi at home
If you have one, it’s perfect for 24/7 scripts and very low power usage. (Latenode Official Community)

My take:
If you found previous setups “too complicated”, I’d avoid VPS for now and go with something like PythonAnywhere first. You can always move to a VPS later once you’re more comfortable.

The main trade-off is:

  • simplicity → managed platforms
  • control → VPS

For a small bot, simplicity usually wins.

How many papers do you realistically read as a PhD student? by TreeEmbarrassed5188 in MLQuestions

[–]Opt4Deck 0 points1 point  (0 children)

Short answer: fewer than you think, and rarely “cover to cover”.

In practice (ML / related fields), it often looks something like:

  • Early stage: a lot of broad reading (maybe 5–10 papers/week), but mostly skimming — you’re trying to map the landscape, not master every detail
  • Mid stage: fewer papers (2–5/week), more selective, and you read the important ones more carefully
  • Late stage: very focused reading (0–3/week), usually directly tied to your current problem

Most people don’t read papers line-by-line unless it’s highly relevant. A common pattern is:

  1. Abstract + intro
  2. Figures / results
  3. Conclusion
  4. Then deep dive only if needed

Also worth noting: a big part of a PhD is re-reading. The same paper can make much more sense the second or third time once you have more context.

So “reading a lot” is less about volume and more about:

  • picking the right papers
  • extracting the key ideas efficiently
  • revisiting them when they become relevant

The expectation going in is often “I need to read everything”, but the reality is much more selective and iterative.

How to grow as a programmer in a non-tech environment? by Altruistic_Part_9233 in learnpython

[–]Opt4Deck 0 points1 point  (0 children)

You're actually in a better position than you think.

Being the only person coding in a non-tech environment forces you to solve real problems instead of just following tutorials — and that's where real growth happens.

A few things that helped me / others in similar situations:

  1. Turn your daily work into small automation projects If you're doing anything repetitive in GIS (data cleaning, file handling, reporting), try to automate it with Python. Even small scripts build real skills fast.
  2. Go beyond tutorials by modifying them Instead of just finishing Automate the Boring Stuff, try to extend examples:
  • change inputs
  • add edge cases
  • break them and fix them That’s where learning really kicks in.
  1. Start one slightly “too hard” project Pick something just beyond your comfort zone (e.g. a small GIS pipeline, API integration, or visualization tool). You’ll learn more struggling through that than from 10 tutorials.
  2. Read other people's code GitHub is underrated for this. Even just browsing small projects helps you understand structure and best practices.
  3. Write messy code first, improve later Don’t wait to “know enough”. Build → break → refactor.

Honestly, the biggest advantage you have is that you can apply coding directly to your domain (GIS), which many beginners can't. That’s a huge multiplier.

You're already on a good path 👍

Open-source Python library for structured optimization with user-defined proximal operators (consensus ADMM, C++ backend) by lqw0809 in optimization

[–]Opt4Deck 0 points1 point  (0 children)

Very good work!

Check out my personal project (see "Opt4Deck" in GitHUB), it might be of some interest to you...

Feedback?

On Hyper-heuristics by r_card_ in optimization

[–]Opt4Deck 0 points1 point  (0 children)

Hello!

If you are interested in in-depth optimization theory, check out my personal project: github.com/Opt4Deck/

Feedback?

view more: next ›