I have an array defined in MATLAB with dimensions 5×5×5×5, i.e., it is a 4-dimensional array. The numbers of this array define the values a function of 4 variables takes over some domain.
I need to compute the Hessian of this function. I need to compute it at every point in the array (ignoring corner cases, say). To do this, I take the function value at a point, compute the second differences and cross-partials across different dimensions. Then I create a determinant and examine its sign.
How to automate this process? That is, visit each point, and compute the determinant, using diff function in MATLAB? Is there a defined procedure?
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I have the following problem. I have a N x N real matrix called Z(x; t), where x and t might be vectors in general. I have N_s observations (x_k, Z_k), k=1,..., N_s and I'd like to find the vector of parameters t that better approximates the data in the least square sense, which means I want t that minimizes
S(t) = \sum_{k=1}^{N_s} \sum_{i=1}^{N} \sum_{j=1}^N (Z_{k, i j} - Z(x_k; t))^2
This is in general a non-linear fitting of a matrix function. I'm only finding examples in which one has to fit scalar functions which are not immediately generalizable to a matrix function (nor a vector function). I tried using the scipy.optimize.leastsq function, the package symfit and lmfit, but still I don't manage to find a solution. Eventually, I'm ending up writing my own code...any help is appreciated!
You can do curve-fitting with multi-dimensional data. As far as I am aware, none of the low-level algorithms explicitly support multidimensional data, but they do minimize a one-dimensional array in the least-squares sense. And the fitting methods do not really care about the "independent variable(s)" x except in that they help you calculate the array to be minimized - perhaps to calculate a model function to match to y data.
That is to say: if you can write a function that would take the parameter values and calculate the matrix to be minimized, just flatten that 2-d (on n-d) array to one dimension. The fit will not mind.
I need to minimize a vector function at all points of x f(x) = a+bx+cx^2+d*x^3. The values of a,b,c,d has to be optimized for the same. Is their any algorithm in matlab that can do the same? I used gamultiobj algorithm for the same, but it generated a large number of these values for each pareto point. I just need a single set of value for the same.
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.
I would like to use knnimpute to fill some missing values in my dataset. Thing is, I would like to use my own distance function, instead of the typical ones (Euclidean, Manhattan...).
For what I've read, knnimpute allows me to use a function handle, that calculates the distance according to Heterogeneous Euclidean-Overlap Metric (HEOM)
I've implemented this function as a regular function, but not as a handle function. So, I cannot use the distance matrix from my "normal" function, because this has to be done inside knnimpute, somehow, as a handler...
I'm confuse, can someone help me understand what I need to do?
As long as your implementation of a distance function has the same signature as the standard distance functions, then you should be able to easily pass your function in.
From the knnimpute documentation (matlab knnimpute) it states that you can pass "A handle to a distance function, specified using #, for example, #distfun." It then refers the reader to the pdist function which provides more details (matlab pdist) about the custom distance function:
A distance function specified using #:
D = pdist(X,#distfun)
A distance function must be of form
d2 = distfun(XI,XJ)
taking as arguments a 1-by-n vector XI, corresponding to a single row of X, and an m2-by-n matrix XJ, corresponding to multiple rows of X. distfun must accept a matrix XJ with an arbitrary number of rows. distfun must return an m2-by-1 vector of distances d2, whose kth element is the distance between XI and XJ(k,:).
So as long as your distance function, as defined in your *.m file matches this signature and so can support these inputs, then there shouldn't be any problems.
Suppose that your distance function is in the mydistFunc.m file, and it's signature matches the above requirements, then all you should need to do is:
% call knnimpute with the data and your function
knnimpute(inputData,'Distance',#mydistFunc);
I am doing my dissertation now. I stuck with a integral. My function is defined as
myfun =(exp(t*Q)*V*x)(j);
where Q and V are a matrix (n*n), x is a vector which elements are 1, then after calculation we get the j_th element of that vector then I need to integrate the function against t.
I want to use the quad in the matlab. However the point is that it will report the inner matrix is not the same size. Since A here is not a number ?....
How can I do this. Otherwise I could only write a loop against t itself, which is extremely slow.
Thanks
You can use SUBSREF for this (you still neet to loop over all j's, though):
myfunOfT = #(t)(subsref(exp(t*Q)*V*x,struct('type','()','subs',j);
This returns the value of the jth element of the array at time t.