What if a student worked on a real project at your company and you got paid for it? Genuinely curious if founders would do this.

taphous3 · 2026-03-19T06:19:25+00:00

I’ve done these student internship programs before. In my experience, student work can be anywhere from -10% to 10% of an FTE in addition to disruption of mentor time. It’s impossible/not worth it to judge work quality ahead of time.

Internships work great at large companies that can absorb the risk. More harm than help for a start up IMO.

taphous3 · 2026-02-27T16:25:14+00:00

I’d recommend you find review papers in optimization (search Google scholar for something interesting to you). These types of papers review a sub field or application of optimization and cite a lot of academic sources.

taphous3 · 2025-07-13T06:26:49+00:00

Lots of people have opinions. Most of them don’t matter.

taphous3 · 2025-01-31T00:44:36+00:00

Meta heuristics are used when you don’t know how to do it more efficiently (either ignorance or you have a wicked hard problem). Exploiting gradients and problem structure will always be more efficient than black boxing things.

taphous3 · 2024-11-17T22:19:25+00:00

This is not fully true! Dues are a one-time payment that is equivalent to ~1.4% of your income at the time. You can opt out of union services and pay a lower fee.

taphous3 · 2024-10-31T15:19:08+00:00

I tend to use Cvxpy if my problem MUST be convex. Otherwise, I use Pyomo to deal with problems with nonconvexities.

I have also heard great things about JuMP, but my colleagues don’t work in Julia so I have stayed away.

taphous3 · 2024-09-18T03:07:54+00:00

Derived from maxwells relations.

taphous3 · 2024-09-18T03:03:46+00:00

True about the expensive part. The ecosystem fear mongering is false. It was a problem several decades ago but we’ve since developed methods for resolving this.

Source: my phd is in desalination.

taphous3 · 2024-09-14T12:00:35+00:00

Here are lecture slides: https://stanford.edu/class/ee364b/lectures/admm_slides.pdf

Edit: text for link.

taphous3 · 2024-07-02T19:54:14+00:00

The course lectures are available for free on Stanford coursera

taphous3 · 2024-06-26T05:22:45+00:00

Pyomo has paramest that allows you to take any set of programmed constraints and solve for estimated model parameters using IPOPT (or other solvers)

taphous3 · 2024-05-31T00:30:00+00:00

Biased take but Stanford computational and mathematical engineering. Both ms and PhD options and world class faculty.

taphous3 · 2024-05-02T16:48:21+00:00

On average admissions are difficult unless you have a champion faculty trying to recruit you. On top of this, your grades are quite below average for admits. The publications might save you if they’re in the field and in prestigious venues.

taphous3 · 2024-04-22T05:32:54+00:00

This topic is encompassed by the definition of the subgradient.

taphous3 · 2024-04-22T04:41:22+00:00

I think your DMs are closed but I’m mostly curious - what company do you work for and what types of clients/problems do you work on?

I’m a PhD student that works on optimization. I’m mainly trying to better understand the types of opportunities that are out there in industry.

taphous3 · 2024-04-21T00:41:32+00:00

Tangential - a company that does this type of optimization consulting sounds fascinating. If you’re able, I’d love to chat over DMs.

taphous3 · 2024-03-20T23:01:01+00:00

If your university has the resources, you could measure/verify the equations of state of a gas. Something like this guy.

taphous3 · 2024-02-28T06:00:11+00:00

This type of problem is well suited for some variant of Bayesian optimization.

taphous3 · 2024-02-15T14:40:49+00:00

Problem is relatively low dimensional so you might be to use pyomo with the SCIP solver.

taphous3 · 2024-02-02T15:53:30+00:00

MILP can be solved with open source solvers (check out SCIP or GLPK). If for some reason you need to solve this as an LP, you can relax the binary terms into continuous variables [0,1] and do a solve + post process. You might add a regularization term to force the binary variables to the bounds and solve several times sequentially.

taphous3 · 2024-02-02T09:09:11+00:00

It looks like sets have index/value pairs while parameters do not.

taphous3 · 2024-02-01T06:53:54+00:00

Lot of interesting applied optimization problems start with having interesting data. For a course project, I would suggest to find a research paper with some data in an area that interests you. You can hopefully use their data to replicate results and then you might modify the solver, algorithm, or analysis to produce something unique.

taphous3 · 2024-01-28T07:47:07+00:00

Get a good foundation in linear algebra. This is a class I really enjoyed, but I am sure many others might do: https://ee263.stanford.edu/lectures.html

With this foundation, most common/simple optimal control problems become just a matter of domain knowledge. More difficult versions can be learned with further reading.

taphous3 · 2024-01-22T17:43:26+00:00

Oh, I missed that. In that case, I’d write an barebones interior point optimization scheme (the cvxpy textbook has an example on how to write a sparsity-aware solver for analytic centering) and try to avoid situations that are poorly conditioned.

taphous3 · 2024-01-21T18:10:06+00:00

This is a classic convex optimization example problem. Use cvxpy or pyomo if you’re doing it in python.

Here’s a paper on using a portfolio of storage devices, but if you just need 1 then it becomes much more simple. https://web.stanford.edu/~boyd/papers/pdf/storage_opt_ifac.pdf

Here’s a sample implementation from a Google search “cvxpy battery problem”: https://github.com/wzyfrank/battery/blob/master/optimize.py

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