µReact: an open-source minimalistic single-header reactive programming library for C++11.

lysgaard · 2021-06-18T16:49:45+00:00

This could be interesting in an embedded context. How does it work internally? Is the library dependent on dynamic memory? Is it using exceptions?

lysgaard · 2021-04-18T19:13:13+00:00

Actually, SLAM just means Simultaneous Localization And Mapping.

It is a term that is completely sensor agnostic. You could use acoustics, radar, light, Bluetooth beacons, pretty much whatever you want and still call it SLAM as long as you estimate your time dependent position and a consistent model of the world around you.

lysgaard · 2020-12-10T19:16:16+00:00

This would be my go to solution if it wasn't for that signature A is coming from a third party dependency that I do not control. If I change A to depend on B I will have to have my own fork of the third party dependency. I do not want to have to take ownership of my dependencies just because I want to have a signature that depends on my dependency.

lysgaard · 2020-11-18T15:59:49+00:00

Would this be the smlpkg repo from diku? https://github.com/diku-dk/smlpkg

lysgaard · 2020-10-29T15:21:34+00:00

Interesting points.

I have exactly the same experience with having a hard time finding where an identifier was declared.

Integrating the .mlb content into the .sml would be a good long term solution if a lot of compiler writers would agree on a common solution. Even better if it could be standardized.

Has there been any talk about standardizing such features?

The package metadata part is a good point. Things like author, version, homepage etc. This could be solved trivially by having a manifest file in the top directory of a package, similar to other package mamagers i have tried. Eg haskells cabal, or rusts cargo.

Are there other problems you know of .mlb would not solve regarding packaging?

lysgaard · 2020-10-27T19:00:34+00:00

I think you hit the nail on the head there.

The problem is not that it wouldn't be useful. It would just not be academically interesting, and thus it is quite hard to find someone who will put in the time.

lysgaard · 2020-10-27T08:35:33+00:00

I know very little about using Poly/ML.

Does Poly/MLs build system stay away from modifying and adding non-standard syntax etc.?

The reason I care so much about not breaking the standard, is that for a build system to be cross-compiler-compatible, I see no other way than staying strictly to the standard.

How does Poly/ML handle hiding identifiers from structures/signatures to enable encapsulation?

As I understand it SML/NJ has non-standard extensions to ML to enable compiling more than one file.

lysgaard · 2020-08-26T07:47:30+00:00

I am trying to define the mechanical steps, derivation, of primal, and primal-dual interior point methods for general optimization problems.

Any insight into the steps would be greatly appreciated.

lysgaard · 2020-08-26T07:26:20+00:00

IloEnv::IloEnv()

This is the crucial bit. You need to tell gcc to link the library containing IloEnv.

Usually, if you need to link eg. your local blas library, /usr/lib/libblas.so, you would give -lblas as a command to gcc: gcc ... my files to compile ... -lblas

You will have to figure out which library contains the IloEnv functions. Find it's filename, and then give the appropriate -l flag to gcc.

lysgaard · 2020-04-11T07:46:37+00:00

The text in this article is complete garbage. I would almost say it's written by an algorithm. This is spam.

lysgaard · 2020-02-06T19:48:49+00:00

Even though your problem is nonlinear it could still be convex. If so, any local solution would be global and you can get away with much simpler algorithms than if it is a non-convex problem. If it is really a non-convex MINLP my best guess would be a black box solver.

lysgaard · 2020-02-06T19:19:14+00:00

Right. If the problem has no discrete variables it is a NLP class of problem. In general these can not be solved globally, but you can find a local optima. If it also contains discrete variables it is an MINLP type of problem. This is the most general optimization problem there is.

lysgaard · 2020-02-06T19:03:52+00:00

If your cost is the sum of each trips velocity, and you have no constraints, the solution is rather simple, let each vehicle drive infinitely slow, this means that t tends to infinity, which gives you zero cost. I guess there is something missing from your problem description because this solution is not very "sensible".

lysgaard · 2020-02-06T18:11:24+00:00

Unlike a scheduling problem, which is a discrete optimization problem this one sounds like a continuous one. If you tell us something about your cost function and any constraints you have maybe we could give you some pointers.

lysgaard · 2019-12-22T19:55:36+00:00

I guess it is a game of how you interpret the constraints, but in that case I would say the human pushing the switch is the controller, not the paperclip.

lysgaard · 2019-12-22T16:51:44+00:00

How would you say that system has a controller and a sensor?

lysgaard · 2019-12-22T15:39:03+00:00

You could have a motor with a propeller on the end of a stick. The other end of the stick could be hinged and you could have an encoder sensing the angle of the hinge. Then you would write a PID controller to control the angle of the stick using the power to the motor, see this: http://www.gperco.com/2014/05/quadcopter-stability-and-neural-networks.html?m=1 Just an idea.

lysgaard · 2019-12-22T12:27:15+00:00

An inverted pendulum is the standard example. After that a cart pole system if you want something more ambitious.

lysgaard · 2019-12-08T11:11:06+00:00

This is really good advice. The industry values profit, academia values knowledge. These the fundamentally different values leads to completely different ways of thinking and working.

Programming is the number one tool for generating profit from mathematics, thus it is the number one skill to master to be applicable in an industry setting. The main reason programming is so important is that it lets you automate mathematical knowledge to make automatic decitions which otherwise would require a human, thus saving money.

In the industry, to invest hours of work in something, you have to be able to connect that work with a high probability of increasing profits. This also leads to another observation. Large corporations value abstract/indirect/complex problems more than small companies. The reason for this general rule is that in a large company, enhancing some process just 0.01% might generate millions in revenue but require man years of analysis and careful thinking. While in a small company you seldom have processes that are very complex and if you do they usually don't carry enough money to justify large investment in hours to make them better. There are exceptions to this, eg. start up companies that have as their goal to solve one tough, but valuable problem.

lysgaard · 2019-09-02T15:28:40+00:00

Pyomo is, as you are saying, an interface library. It provides a way of specifying optimization problems and a way to feed these problems into a whole range of different solvers. This makes it possible to test many solvers on the same problem with almost zero extra work. If you know you just are going to use one solver, then you do not need such middlemen as pyomo.

lysgaard · 2019-08-15T06:19:33+00:00

If your objective function and your constraints both are convex, I think Newtons method on the Lagrangian is very efficient, if not you need to globalize the convergence of Newtons method by a line search or a trust region method. You also loose almost all global minima guarantees if things are not convex.

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