I would like to find global minima using an objective function I defined.
The code I used is:
opts = optimoptions(#fmincon,'Algorithm','interior-point');
gs = GlobalSearch;
problem = createOptimProblem('fmincon','x0',a,'objective', #(s) obj(supdata,s),'options',opts);
[xg, fg, exitflag, output, solutions ]=run(gs, problem)
The problem is that supdata is for a specific firm, and I need to get the optimal result for each firm (there are thousands of them). I want to apply this code using a big data input (which I already have, in a cell array, with each cell being a specific firm), and the output xg fg... are also varies among firms.
I was hoping to use a loop inside the function obj, but people here suggested that I to change my function to use cellfun: see my original question. But I don't know how to incorporate it into globalsearch. It is more complicated than just calling the solver fmincon.
Related
I have a differential equation that's a function of around 30 constants. The differential equation is a system of (N^2+1) equations (where N is typically 4). Solving this system produces N^2+1 functions.
Often I want to see how the solution of the differential equation functionally depends on constants. For example, I might want to plot the maximum value of one of the output functions and see how that maximum changes for each solution of the differential equation as I linearly increase one of the input constants.
Is there a particularly clean method of doing this?
Right now I turn my differential-equation-solving script into a large function that returns an array of output functions. (Some of the inputs are vectors & matrices). For example:
for i = 1:N
[OutputArray1(i, :), OutputArray2(i, :), OutputArray3(i, :), OutputArray4(i, :), OutputArray5(i, :)] = DE_Simulation(Parameter1Array(i));
end
Here I loop through the function. The function solves a differential equation, and then returns the set of solution functions for that input parameter, and then each is appended as a row to a matrix.
There are a few issues I have with my method:
If I want to see the solution to the differential equation for a different parameter, I have to redefine the function so that it is an input of one of the thirty other parameters. For the sake of code readability, I cannot see myself explicitly writing all of the input parameters as individual inputs. (Although I've read that structures might be helpful here, but I'm not sure how that would be implemented.)
I typically get lost in parameter space and often have to update the same parameter across multiple scripts. I have a script that runs the differential-equation-solving function, and I have a second script that plots the set of simulated data. (And I will save the local variables to a file so that I can load them explicitly for plotting, but I often get lost figuring out which file is associated with what set of parameters). The remaining parameters that are not in the input of the function are inside the function itself. I've tried making the parameters global, but doing so drastically slows down the speed of my code. Additionally, some of the inputs are arrays I would like to plot and see before running the solver. (Some of the inputs are time-dependent boundary conditions, and I often want to see what they look like first.)
I'm trying to figure out a good method for me to keep track of everything. I'm trying to come up with a smart method of saving generated figures with a file tag that displays all the parameters associated with that figure. I can save such a file as a notepad file with a generic tagging-number that's listed in the title of the figure, but I feel like this is an awkward system. It's particularly awkward because it's not easy to see what's different about a long list of 30+ parameters.
Overall, I feel as though what I'm doing is fairly simple, yet I feel as though I don't have a good coding methodology and consequently end up wasting a lot of time saving almost-identical functions and scripts to solve fairly simple tasks.
It seems like what you really want here is something that deals with N-D arrays instead of splitting up the outputs.
If all of the OutputArray_ variables have the same number of rows, then the line
for i = 1:N
[OutputArray1(i, :), OutputArray2(i, :), OutputArray3(i, :), OutputArray4(i, :), OutputArray5(i, :)] = DE_Simulation(Parameter1Array(i));
end
seems to suggest that what you really want your function to return is an M x K array (where in this case, K = 5), and you want to pack that output into an M x K x N array. That is, it seems like you'd want to refactor your DE_Simulation to give you something like
for i = 1:N
OutputArray(:,:,i) = DE_Simulation(Parameter1Array(i));
end
If they aren't the same size, then a struct or a table is probably the best way to go, as you could assign to one element of the struct array per loop iteration or one row of the table per loop iteration (the table approach would assume that the size of the variables doesn't change from iteration to iteration).
If, for some reason, you really need to have these as separate outputs (and perhaps later as separate inputs), then what you probably want is a cell array. In that case you'd be able to deal with the variable number of inputs doing something like
for i = 1:N
[OutputArray{i, 1:K}] = DE_Simulation(Parameter1Array(i));
end
I hesitate to even write that, though, because this almost certainly seems like the wrong data structure for what you're trying to do.
Hi, I am trying to write a function as per the question. I have tried to create four sub-matrices which are the reverse of each other and then multiply to give the products demanded by the question. My attempt:
function T = custom_blocksT(n,m)
T(1:end,end-1:1);
T(1:end,end:-1:1)*2;
T(1:end,end:-1:1)*3;
T(1:end,end:-1:1)*4;
What I'm unsure of is
(i) What do the the indivual sub-matrices(T(1:end,end-1:1);)need to be equal to? I was thinking of(1:3)?
(ii) I tried to create a generic sub-matrix which can take any size matrix input using end was this correct or can't you do that? I keep getting this error
Undefined function or variable 'T'.
Error in custom_blocksT (line 2)
T(1:end,end-1:1);
I have searched the Matlab documentation and stacked overflow, but the problem is I'm not quite sure what I'm supposed to be looking for in terms of solving this question.
If someone could help me I would be very thankfull.
There are many problems with your function:
function T = custom_blocksT(n,m)
T(1:end,end-1:1);
T(1:end,end:-1:1)*2;
T(1:end,end:-1:1)*3;
T(1:end,end:-1:1)*4;
end
This is an extremely basic question, I highly recommend you find and work through some very basic MATLAB tutorials before continuing, even before reading this answer to be honest.
That said here is what you should have done and a bit of what you did wrong:
First, you are getting the error that T dos not exist because it doesn't. The only variables that exist in your function are those that you create in the function or those that are passed in as parameters. You should have passed in T as a parameter, but instead you passed in n and m which you don't use.
In the question, they call the function using the example:
custom_blocks([1:3;3:-1:1])
So you can see that they are only passing in one variable, your function takes two and that's already a problem. The one variable is the matrix, not it's dimensions. And the matrix they are passing in is [1:3;3:-1:1] which if you type in the command line you will see gives you
[1 2 3
3 2 1]
So for your first line to take in one argument which is that matrix it should rather read
function TOut = custom_blocks(TIn)
Now what they are asking you to do is create a matrix, TOut, which is just different multiples of TIn concatenated.
What you've done with say TIn(1:end,end-1:1)*2; is just ask MATLAB to multiple TIn by 2 (that's the only correct bit) but then do nothing with it. Furthermore, indexing the rows by 1:end will do what you want (i.e. request all the rows) but in MATLAB you can actually just use : for that. Indexing the columns by end-1:1 will also call all the columns, but in reverse order. So in effect you are flipping your matrix left-to-right which I'm sure is not what you wanted. So you could have just written TIn(:,:) but since that's just requesting the entire matrix unchanged you could actually just write TIn.
So now to multiply and concatenate (i.e. stick together) you do this
TOut = [TIn, TIn*2; TIn*3, TIn*4]
The [] is like a concatenate operation where , is for horizontal and ; is for vertical concatenation.
Putting it all together:
function TOut = custom_blocks(TIn)
TOut = [TIn, TIn*2; TIn*3, TIn*4];
end
I am trying to understand the following commands of a MATLAB script :
global operatorObj
calcEVR_handles = operatorObj.calcEVR_handles;
m = operatorObj.nInputs
E = zeros(m,1);
V = zeros(m,1);
R = zeros(m,m);
for i=1:m
[E(i), V(i), R(i,i)] = calcEVR_handles{i}(t,x);
end
What can calcEVR_handles be, if t is a float and x is a vector?
calcEVR_handles (to me) looks like a cell array where each element is a handle to a function. Each element in calcEVR_handles is an anonymous function that takes in a single value t and a single vector x. As such, by doing calcEVR_handles{i}, you would access the corresponding function stored at the ith element in the cell array. Once you have access, you then pass your parameters to this function and it gives you those three outputs.
To show you an example of this working, consider the following cell array that works similarly to calcEVR_handles.
calcCellFunc = {#sin, #cos, #tan};
This is a three element cell array, where each element is a handle to a function. The # is a special character in MATLAB that denotes that you are creating a handle to a function. It's also used to create anonymous functions, but let's shelve that for this answer. You can read more about it here if you want to delve into more detail regarding this.
Back to our cell array of handles, we will make handles for sin, cos and tan. You can then iterate over your cell array by accessing the function you want by calcCellFunc{idx} where idx is the element you want in the cell array. This will ultimately give you the function stored at index idx. Once you do that, you can then call the function and specify whatever inputs you want (or none if it doesn't take any inputs). Here's a quick example for you. Let's create a random 5 x 5 matrix, and run through each function with this matrix serving as the input. We then take each of these outputs and store them into a corresponding slot in an output cell array. As such:
rng(123); %// Set seed for reproducibility
M = rand(5);
calcCellFunc = {#sin, #cos, #tan};
out = cell(1, numel(calcCellFunc)); %// To store the results for each function
for idx = 1 : numel(calcCellFunc)
out{idx} = calcCellFunc{idx}(M); %// Get the function, then pass
%// the matrix M to it
end
If you want to make things clear, you could split up the out statement to this instead:
func = calcCellFunc{idx}; %// Get access to the function
out{idx} = func(M); %// Pass M to this function
If you're new to handles / anonymous functions, you should probably use the above code first to make it explicitly clear on what MATLAB is doing. You are first getting access to the function you want that is stored in the cell array, and then you pass your arguments to this function.
If we display the output, we get:
>> celldisp(out)
out{1} =
0.6415 0.4106 0.3365 0.6728 0.5927
0.2823 0.8309 0.6662 0.1815 0.7509
0.2249 0.6325 0.4246 0.1746 0.6627
0.5238 0.4626 0.0596 0.5069 0.5737
0.6590 0.3821 0.3876 0.5071 0.6612
out{2} =
0.7671 0.9118 0.9417 0.7398 0.8054
0.9593 0.5564 0.7458 0.9834 0.6604
0.9744 0.7745 0.9054 0.9846 0.7489
0.8518 0.8866 0.9982 0.8620 0.8191
0.7522 0.9241 0.9218 0.8619 0.7502
out{3} =
0.8363 0.4503 0.3573 0.9094 0.7359
0.2942 1.4934 0.8932 0.1845 1.1370
0.2308 0.8167 0.4690 0.1773 0.8850
0.6149 0.5218 0.0597 0.5880 0.7004
0.8761 0.4135 0.4205 0.5884 0.8814
The first element of the output cell array has the output when you pass M to sin, the second when you pass M to cos, and the third when you pass M to tan.
So the next question you're asking... why is this useful?
Point #1 - Nix the copying and pasting
This kind of code writing is very useful because if you want to use the same inputs and supply them to many different functions, we would naturally be inclined to do some copying and pasting. Take each of your function names, and create a single line for each. Each line would call the corresponding function you want, followed by the input arguments. This can become quite tedious, and so one smart way to do it would be to place your function name as a handle into a cell array, and to write one for loop that goes over all of the functions dynamically. You could even explore cellfun and escape using the for loop to iterate over all of the function handles too, but I'll leave that for you to read up on.
In this way, you have very maintainable code and if you want to remove functions that don't need to be run, just remove the handles from the cell array rather than scrolling down to where the line that invokes this function is located and removing that.
This is actually a very common technique in computer science / software engineering in general. In fact, this is actually quite close to what are known as function pointers. This is MATLAB's cheap way of doing it, but the logic behind this is essentially the same.
Point #2 - Higher Order Functions
Another way this is useful is if you have a function where one (or more than one!) of the inputs is a function, and you also specify inputs into this function as additional parameters to this function. This is what is known as a higher order function. The outputs would be based on using this input function, and the additional inputs you specify to it and the outputs are based on using this input function and the inputs you specify for this function.
One very good example is the fzero function in MATLAB. The goal is to find the root of a non-linear function, and the first parameter is a handle to a function that you specify. The base behaviour behind how fzero works is the same no matter what the function is. All you have to do is specify the function you want to solve and the initial guess of where you think this root is.
All in all, anonymous functions are very useful.
I am trying to vectorise a for loop. I have a set of coordinates listed in a [68x200] matrix called plt2, and I have another set of coordinates listed in a [400x1] matrix called trans1. I want to create a three dimensional array called dist1, where in dist1(:,:,1) I have all of the values of plt2 with the first value of trans1 subtracted, all the way through to the end of trans1. I have a for loop like this which works but is very slow:
for i=1:source_points;
dist1(:,:,i)=plt2-trans1(i,1);
end
Thanks for any help.
If I understood correctly, this can be easily solved with bsxfun:
dist1 = bsxfun(#minus, plt2, shiftdim(trans1,-2));
Or, if speed is important, use this equivalent version (thanks to #chappjc), which seems to be much faster:
dist1 = bsxfun(#minus, plt2, reshape(trans1,1,1,[]));
In general, bsxfun is a very useful function for cases like this. Its behaviour can be summarized as follows: for any singleton dimension of any of its two input arrays, it applies an "implicit" for loop to the other array along the same dimension. See the doc for further details.
Vectorizing is a good first optimization, and is usually much easier than going all in writing your own compiled mex-function (in c).
However, the golden middle-way for power users is Matlab Coder (this also applies to slightly harder problems than the one posted, where vectorization is more or less impossible). First, create a small m-file function around the slow code, in your case:
function dist1 = do_some_stuff(source_points,dist1,plt2,trans1)
for i=1:source_points;
dist1(:,:,i)=plt2-trans1(i,1);
end
Then create a simple wrapper function which calls do_some_stuff as well as defines the inputs. This file should really be only 5 rows, with only the bare essentials needed. Matlab Coder uses the wrapper function to understand what typical proper inputs to do_some_stuff are.
You can now fire up the Matlab Coder gui from the Apps section and simply add do_some_stuff under Entry-Point Files. Press Autodefine types and select your wrapper function. Go to build and press build, and you are good to go! This approach usually bumps up the execution speed substantially with almost no effort.
BR
Magnus
I've got an ODE system working perfectly. But now, I want in each iteration, sort in ascending order the solution vector. I've tried many ways but I could not do it. Does anyone know how to do?
Here is a simplified code:
function dtemp = tanque1(t,temp)
for i=1:N
if i==1
dtemp(i)=(((-k(i)*At*(temp(i)-temp(i+1)))/(y))-(U*As(i)*(temp(i)-Tamb)))/(ro(i)*vol_nodo*cp(i));
end
if i>1 && i<N
dtemp(i)=(((k(i)*At*(temp(i-1)-temp(i)))/(y))-((k(i)*At*(temp(i)-temp(i+1)))/(y))-(U*As(i)*(temp(i)-Tamb)))/(ro(i)*vol_nodo*cp(i));
end
if i==N
dtemp(i)=(((k(i)*At*(temp(i-1)-temp(i)))/(y))-(U*As(i)*(temp(i)-Tamb)))/(ro(i)*vol_nodo*cp(i));
end
end
end
Test Script:
inicial=343.15*ones(200,1);
[t temp]=ode45(#tanque1,0:360:18000,inicial);
It looks like you have three different sets of differential equations depending on the index i of the solution vector. I don't think you mean "sort," but rather a more efficient way to implement what you've already done - basically vectorization. Provided I haven't accidentally made any typos (you should check), the following should do what you need:
function dtemp = tanque1(t,temp)
dtemp(1) = (-k(1)*At*(temp(1)-temp(2))/y-U*As(1)*(temp(1)-Tamb))/(ro(1)*vol_nodo*cp(1));
dtemp(2:N-1) = (k(2:N-1).*(diff(temp(1:N-1))-diff(temp(2:N)))*At/y-U*As(2:N-1).*(temp(2:N-1)-Tamb))./(vol_nodo*ro(2:N-1).*cp(2:N-1));
dtemp(N) = (k(N)*At*(temp(N-1)-temp(N))/y-U*As(N)*(temp(N)-Tamb))/(ro(N)*vol_nodo*cp(N));
You'll still need to define N and the other parameters and ensure that temp is returned as a column vector. You could also try replacing N with the end keyword, which might be faster. The two uses of diff make the code shorter, but, depending on the value of N, they may also speed up the calculation. They could be replaced with temp(1:N-2)-temp(2:N-1) and temp(2:N-1)-temp(3:N). It may be possible to collapse these down to a single vectorized equation, but I'll leave that as an exercise for you to attempt if you like.
Note that I also removed a great many unnecessary parentheses for clarity. As you learn Matlab you'll to get used to the order of operations and figure out when parentheses are needed.