I am trying to understand Generalized Simulated Annealing (https://arxiv.org/pdf/cond-mat/9501047.pdf) and Dual Annealing (Dual Annealing) for my own problem. It is calculating (through a graph) some values in the form of a 64 bit number, and my cost/error function is XOR of that calculated 64 bit number and another 64 bit integer. I am trying to apply Generalized SA to optimize my graph structure to get the best results. Any help is highly appreciated. Thank you in advance.
I tried to read the paper mentioned in the question and read ML mastery blot also mentioned in the question.
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I'm implementing LDA using Java. I know how the algorithm works. In the end of the training (the given iterations) I will get 2 matrices (topic-word and document-topic) that represent the set of the input documents.
My problem is that when I input a new document (query) I want to use these matrices (or any other way) to get the document-topic vector of that query. How would I do that?
Are you using Variational Inference or Gibbs Sampling?
For Gibbs Sampling a typical approach is adding the new document/s to the inference, and only updating its own counters, keeping constant the counters for the documents you used to learn the model.
This is specified in equations 84 and 85 in Parameter Estimation for Text Analysis
I guess there has to be a similar approach in VI LDA.
I'm a bit of a novice when it comes to implementing FFTs in general, but I have most of the basic ideas down I think. In this specific case, I've an implementation of the number theoretic transform on the 257 finite field. It's basically your typical Radix-2 Cooley-Tukey FFT. What Id like to know is either: is there a good alternative to the Cooley-Tukey Radix-2 that's better suited to doing this particular NTT efficiently (if the answer is an unqualified yes or a yes conditional on something not entirely within the scope of this question, I'm interested in hearing about either), or are there things specific to a Mersenne NTT that allow for a more efficient implementation than a more general case?
I'd say that for dyadic length FFT there is nothing better than Cooley-Tukey.
This has nothing directly to to with Mersenne numbers, any number field with modulus 2^(m*2^n)+1 qualifies. I=2^(m*2^(n-1)) is the complex unit, I^2=2^(m*2^n)=-1 mod (2^(m*2^n)+1), and q=2^(2*m) is a primitive 2^n-th root of unity.
For inspiration for the second point see Section 1 of Schönhage: Asymtotically fast algorithms for the numerical multiplication ..., with overall summary of fast multiplications
I am a beginner to MIPS studying comp. architecture. The problem, I am stuck at is.
I was asked by my teacher to prepare a SOP and POS for OR instruction in MIPS. ORing 2 registers. I am confused that how to do so. Should I make a k-map?? If so.. then again stuck.. when considering variables.. Should I consider 32 variables in k-map? or just 2 (A and B)?? Can any one help.
P.S. This is just my first question to stack.. Otherwise I was getting ans to all questions in search.
Thank you
I have tried searching it on Google and over where as well. Unfortunately it did not answer my question
I average over a multiple solutions of ODEs that have different initial conditions, so it's important for all of the solutions to have values at the same times; for example, at an increment of 0.01.
i've been using ODE routines from numerical recipes 3 (nr3). they do adaptive size-step and use the calculated values to do the same order of interpolation. i can't use them because they conflict with boost. are there any other similar routines?
i looked at GSL, it's very nice but it doesn't have a built in interpolation. one way i can do it is solve the ODE with an adaptive size and than run Akima interpolation. But it seems like nr3 solution would be faster and more accurate.
You can use odeint. It has Dopri5, Rosenbrock4 and Burlish-Stoer for dense output.
I have used DOPRI5 from http://www.unige.ch/~hairer/software.html with dense output = interpolation. I found it reliable. I used the original version (in Fortran); there is also a C version on the same webpage which I haven't used myself but I seem to remember that people were happy with it.
I am working on a project to create a generic equation solver... envision this to take the form of 25-30 equations that will be saved in a table- variable names along with the operators.
I would then call this table for solving any equation with a missing variable and it would move operators/ other pieces to the other side of the missing variable
e.g. 2x+ 3y=z and if x were missing variable. I would call equation with values for y and z and it would convert to solve for x=(z-3y)/2
equations could be linear, polynomial, binary(yes/no result)...
i am not sure if i can get any light-weight library available or whether this needs to built from scratch... any pointers or guidance will be appreciated
See Maxima.
I rather like it for my symbolic computation needs.
If such a general black-box algorithm could be made accurate, robust and stable, pigs could fly. Solutions can be nonexistent, multiple, parametrized, etc.
Even for linear equations it gets tricky to do it right.
Your best bet is some form of Newton algorithm, but generally you tailor it to your problem at hand.
EDIT: I didn't see you wanted something symbolic, rather than numerical. It's another bag of worms.