I have a Function V which depends on two variables v1 and v2 and a parameter-Array p containing 15 Parameters.
I want to Minimize my Function V regarding v1 and v2, but there is no closed expression for my Function, so I can't build and use the Derivatives.
The Problem is the following : For caluclating the Value of my Function I need the Eigenvalues of two 4x4 Matrices (which should be symmetric and real by concept, but sometimes the EigenSolver does not get real Eigenvalues). These Eigenvalues I calculate with the Eigen Package. The entries of the Matrices are given by v1,v2 and p.
There are certain Input Sets for which some of these Eigenvalues become negative. These are Input Sets which I want to ignore for my calculation as they will lead to an complex Function value and my Function is only allowed to have real values.
Is there a way to include this? My first attempt was a Nelder-Mead-Simplex Algorithm using the GSL-Library and an way too high Output value for the Function if one of the Eigenvalues becomes negative, but this doesn't work.
Thanks for any suggestions.
For the Nelder-Mead simplex, you could reject new points as vertices for the simplex, unless they have the desired properties.
Your method to artificially increase the function value for forbidden points is also called penalty or barrier function. You might want to re-design your penalty function.
Another optimization method without derivatives is the Simulated Annealing method. Again, you could modify the method to avoid forbidden points.
What do you mean by "doesn't work"? Does it take too long? Are the resulting function values too high?
Depending on the function evaluation cost, it might be an approach to simply scan a 2D interval, evaluate all width x height function values and drill down in the tile with the lowest function values.
Related
I have a problem detecting outliers in a set of data. Let's say I have two arrays x and y, and y is a quadratic function of x. Some of the values of y do not follow this function. How can I detect them?
I tried the rmoutliers function, but it doesn't seem to solve this problem since it only deals with normally distributed data.
Basically, I am trying to study some material behavior. The behavior is represented by y. I use an optimization method to get the different values of y as a function of x. Because sometimes the optimization doesn't yield accurate results, I get outliers.The relationship I am expecting should follow some nearly quadratic function, but the coefficients of this function are variable based one the provided set of data, so I can't use a certain function of x and use it to detect the outliers in the array of y values.
Can someone please explain why the following symmetric function cannot pass a certain limit of negative values?
D = 0.1; l = 4;
c = #(x,v) (v/D).*exp(-v*x/D)./(1-exp(-v*l/D));
v_vec = -25:0.01:25;
figure(2)
hold on
plot(v_vec,c(l,v_vec),'b')
plot(v_vec,c(0,v_vec),'r')
Notice at the figure where the blue line chops, this is where I get inf/nan values.
It seems that Matlab is trying to compute a result that is too large, outputs +inf, and then operates on that, which yields +/- inf and NaNs.
For instance, at v=-25, part of the function computes exp(-(-25)*4/0.1), which is exp(1000), and that outputs +inf. (larger than the largest representable double precision float).
You can potentially solve that problem by rewriting your function to avoid operating of such very large (or very small) numbers, say by reorganising the fraction containing exp() functions.
I did encounter the same hurdle using exp() with arguments triggering overflow. Sometimes it is difficult to trace back numeric imprecision or convergence errors. In principle the function definition using exp() only create intermediate issues as your purpose as a transition function. The intention I guess was to provide a continuous function.
My solution to this problem is to divide the argument into regions and provide in each region an approximation function. In your case zero for negative x and proportional to x for positive x. In between you can use the orginal function. Care should be taken to match the approximation at the borders of the regions and the number of continuous differentiations which is important for convergence in loops.
I have a system of three nonlinear equations with eight unknowns. I'm currently setting each equation equal to a desired value and then using Matlab's fsolve (a numerical solver) to find a solution. Instead of running fsolve in real-time, I'd like to pre-compute solutions for a specific set of values to which I set the equations equal.
Pursuant that goal, I've run the solver over a set of values and created a 3D matrix (N x N x N) which I've attempted to load into eight Simulink 3-D lookup tables, Direct Lookup Table n-D block, so I can fetch each of the eight solved unknowns. It's my understanding the inputs to this block should work the same way I would reference an element in my 3-D array: table(x,y,z) but I'm constantly getting Simulink table input out-of-range errors. I've confirmed the inputs are within the table size, so I'm not sure what's wrong.
This isn't the most elegant implementation, so I'm open to better solutions. Ideally, I'd like to have a Simulink lookup that takes three inputs and returns a vector of the eight solved unknowns, or even better, can do some type of linear interpolation between the three lookup values to return an approximate solution.
Thanks!
I'm solving a second order differential equation in MATLAB using a finite element method, where I write the second order derivative of a function f as:
d^2f/dx^2 = (f_{i}-f_{i-1}/(x_{i}-x{i-1}) - f_{i+1}-f_{i}/(x_{i+1}-x{i})/(x_{i+1}-x{i-1})/2
Now this operation on f can be translated into a matrix, for which I can then find the eigenvectors, which then are the solutions to the given differential equation.
All this works well for a uniform grid of x-values, i.e. same spacing. But when I try to do it for a nonuniform I get oscillations that should not be there, because the values in the matrix are weighted differently depending on how close the neighbouring grid points are.
Is my approach wrong? Should I use some kind of weighting to take care of the nonuniformity?
I am not sure if I got it right, but the relation you wrote:
d^2f/dx^2 = (f_{i}-f_{i-1}/(x_{i}-x{i-1}) - f_{i+1}-f_{i}/(x_{i+1}-x{i})/(x_{i+1}-x{i-1})/2
looks like finite difference NOT finite element!!!
Otherwise, finite element method does not care (much) about the change of element size from one point to the other in most of the mechanics problems.
If you are handling a finite difference problem, the method does not have to have a regular mesh, but the relations have to be written "carefully" in order to avoid the confusion that may be included in the system matrices.
I'm not too familiar with MATLAB or computational mathematics so I was wondering how I might solve an equation involving the sum of squares, where each term involves two vectors- one known and one unknown. This formula is supposed to represent the error and I need to minimize the error. I think I'm supposed to use least squares but I don't know too much about it and I'm wondering what function is best for doing that and what arguments would represent my equation. My teacher also mentioned something about taking derivatives and he formed a matrix using derivatives which confused me even more- am I required to take derivatives?
The problem that you must be trying to solve is
Min u'u = min \sum_i u_i^2, u=y-Xbeta, where u is the error, y is the vector of dependent variables you are trying to explain, X is a matrix of independent variables and beta is the vector you want to estimate.
Since sum u_i^2 is diferentiable (and convex), you can evaluate the minimal of this expression calculating its derivative and making it equal to zero.
If you do that, you find that beta=inv(X'X)X'y. This maybe calculated using the matlab function regress http://www.mathworks.com/help/stats/regress.html or writing this formula in Matlab. However, you should be careful how to evaluate the inverse (X'X) see Most efficient matrix inversion in MATLAB