As noted in this answer by Sam Roberts and this other answer by gnovice, MATLAB's colon operator (start:step:stop) creates a vector of values in a different way that linspace does. In particular, Sam Roberts states:
The colon operator adds increments to the starting point, and subtracts decrements from the end point to reach a middle point. In this way, it ensures that the output vector is as symmetric as possible.
However, offical documentation about this from The MathWorks has been deleted from their site.
If Sam's description is correct, wouldn't the errors in the step sizes be symmetric?
>> step = 1/3;
>> C = 0:step:5;
>> diff(C) - step
ans =
1.0e-15 *
Columns 1 through 10
0 0 0.0555 -0.0555 -0.0555 0.1665 -0.2776 0.6106 -0.2776 0.1665
Columns 11 through 15
0.1665 -0.2776 -0.2776 0.6106 -0.2776
Interesting things to note about the colon operator:
Its values depend on its length:
>> step = 1/3;
>> C = 0:step:5;
>> X = 0:step:3;
>> C(1:10) - X
ans =
1.0e-15 *
0 0 0 0 0 -0.2220 0 -0.4441 0.4441 0
It can generate repeated values if they are rounded:
>> E = 1-eps : eps/4 : 1+eps;
>> E-1
ans =
1.0e-15 *
-0.2220 -0.2220 -0.1110 0 0 0 0 0.2220 0.2220
There is a tolerance for the last value, if the step size creates a value just above the end, this end value is still used:
>> A = 0 : step : 5-2*eps(5)
A =
Columns 1 through 10
0 0.3333 0.6667 1.0000 1.3333 1.6667 2.0000 2.3333 2.6667 3.0000
Columns 11 through 16
3.3333 3.6667 4.0000 4.3333 4.6667 5.0000
>> A(end) == 5 - 2*eps(5)
ans =
logical
1
>> step*15 - 5
ans =
0
The deleted page referred to by Sam's answer is still archived by the Way Back Machine. Luckily, even the attached M-file colonop is there too. And it seems that this function still matches what MATLAB does (I'm on R2017a):
>> all(0:step:5 == colonop(0,step,5))
ans =
logical
1
>> all(-pi:pi/21:pi == colonop(-pi,pi/21,pi))
ans =
logical
1
I'll replicate here what the function does for the general case (there are some shortcuts for generating integer vectors and handling special cases). I'm replacing the function's variable names with more meaningful ones. The inputs are start, step and stop.
First it computes how many steps there are in between start and stop. If the last step exceeds stop by more than a tolerance, it is not taken:
n = round((stop-start)/step);
tol = 2.0*eps*max(abs(start),abs(stop));
sig = sign(step);
if sig*(start+n*step - stop) > tol
n = n - 1;
end
This explains the last observation mentioned in the question.
Next, it computes the value of the last element, and makes sure that it does not exceed the stop value, even if it allowed to go past it in the previous computation.
last = start + n*step;
if sig*(last-stop) > -tol
last = stop;
end
This is why the lasat value in the vector A in the question actually has the stop value as the last value.
Next, it computes the output array in two parts, as advertised: the left and right halves of the array are filled independently:
out = zeros(1,n+1);
k = 0:floor(n/2);
out(1+k) = start + k*step;
out(n+1-k) = last - k*step;
Note that they are not filled by incrementing, but by computing an integer array and multiplying it by the step size, just like linspace does. This exaplains the observation about array E in the question. The difference is that the right half of the array is filled by subtracting those values from the last value.
As a final step, for odd-sized arrays, the middle value is computed separately to ensure it lies exactly half-way the two end points:
if mod(n,2) == 0
out(n/2+1) = (start+last)/2;
end
The full function colonop is copied at the bottom.
Note that filling the left and right side of the array separately does not mean that the errors in step sizes should be perfectly symmetric. These errors are given by roundoff errors. But it does make a difference where the stop point is not reached exactly by the step size, as in the case of array A in the question. In this case, the slightly shorter step size is taken in the middle of the array, rather than at the end:
>> step=1/3;
>> A = 0 : step : 5-2*eps(5);
>> A/step-(0:15)
ans =
1.0e-14 *
Columns 1 through 10
0 0 0 0 0 0 0 -0.0888 -0.4441 -0.5329
Columns 11 through 16
-0.3553 -0.3553 -0.5329 -0.5329 -0.3553 -0.5329
But even in the case where the stop point is reached exactly, some additional error accumulates in the middle. Take for example the array C in the question. This error accumulation does not happen with linspace:
C = 0:1/3:5;
lims = eps(C);
subplot(2,1,1)
plot(diff(C)-1/3,'o-')
hold on
plot(lims,'k:')
plot(-lims,'k:')
plot([1,15],[0,0],'k:')
ylabel('error')
title('0:1/3:5')
L = linspace(0,5,16);
subplot(2,1,2)
plot(diff(L)-1/3,'x-')
hold on
plot(lims,'k:')
plot(-lims,'k:')
plot([1,15],[0,0],'k:')
title('linspace(0,5,16)')
ylabel('error')
colonop:
function out = colonop(start,step,stop)
% COLONOP Demonstrate how the built-in a:d:b is constructed.
%
% v = colonop(a,b) constructs v = a:1:b.
% v = colonop(a,d,b) constructs v = a:d:b.
%
% v = a:d:b is not constructed using repeated addition. If the
% textual representation of d in the source code cannot be
% exactly represented in binary floating point, then repeated
% addition will appear to have accumlated roundoff error. In
% some cases, d may be so small that the floating point number
% nearest a+d is actually a. Here are two imporant examples.
%
% v = 1-eps : eps/4 : 1+eps is the nine floating point numbers
% closest to v = 1 + (-4:1:4)*eps/4. Since the spacing of the
% floating point numbers between 1-eps and 1 is eps/2 and the
% spacing between 1 and 1+eps is eps,
% v = [1-eps 1-eps 1-eps/2 1 1 1 1 1+eps 1+eps].
%
% Even though 0.01 is not exactly represented in binary,
% v = -1 : 0.01 : 1 consists of 201 floating points numbers
% centered symmetrically about zero.
%
% Ideally, in exact arithmetic, for b > a and d > 0,
% v = a:d:b should be the vector of length n+1 generated by
% v = a + (0:n)*d where n = floor((b-a)/d).
% In floating point arithmetic, the delicate computatations
% are the value of n, the value of the right hand end point,
% c = a+n*d, and symmetry about the mid-point.
if nargin < 3
stop = step;
step = 1;
end
tol = 2.0*eps*max(abs(start),abs(stop));
sig = sign(step);
% Exceptional cases.
if ~isfinite(start) || ~isfinite(step) || ~isfinite(stop)
out = NaN;
return
elseif step == 0 || start < stop && step < 0 || stop < start && step > 0
% Result is empty.
out = zeros(1,0);
return
end
% n = number of intervals = length(v) - 1.
if start == floor(start) && step == 1
% Consecutive integers.
n = floor(stop) - start;
elseif start == floor(start) && step == floor(step)
% Integers with spacing > 1.
q = floor(start/step);
r = start - q*step;
n = floor((stop-r)/step) - q;
else
% General case.
n = round((stop-start)/step);
if sig*(start+n*step - stop) > tol
n = n - 1;
end
end
% last = right hand end point.
last = start + n*step;
if sig*(last-stop) > -tol
last = stop;
end
% out should be symmetric about the mid-point.
out = zeros(1,n+1);
k = 0:floor(n/2);
out(1+k) = start + k*step;
out(n+1-k) = last - k*step;
if mod(n,2) == 0
out(n/2+1) = (start+last)/2;
end
Related
I am trying to implement a simplex algorithm following the rules I was given at my optimization course. The problem is
min c'*x s.t.
Ax = b
x >= 0
All vectors are assumes to be columns, ' denotes the transpose. The algorithm should also return the solution to dual LP. The rules to follow are:
Here, A_J denotes columns from A with indices in J and x_J, x_K denotes elements of vector x with indices in J or K respectively. Vector a_s is column s of matrix A.
Now I do not understand how this algorithm takes care of condition x >= 0, but I decided to give it a try and follow it step by step. I used Matlab for this and got the following code.
X = zeros(n, 1);
Y = zeros(m, 1);
% i. Choose starting basis J and K = {1,2,...,n} \ J
J = [4 5 6] % for our problem
K = setdiff(1:n, J)
% this while is for goto
while 1
% ii. Solve system A_J*\bar{x}_J = b.
xbar = A(:,J) \ b
% iii. Calculate value of criterion function with respect to current x_J.
fval = c(J)' * xbar
% iv. Calculate dual solution y from A_J^T*y = c_J.
y = A(:,J)' \ c(J)
% v. Calculate \bar{c}^T = c_K^T - u^T A_K. If \bar{c}^T >= 0, we have
% found the optimal solution. If not, select the smallest s \in K, such
% that c_s < 0. Variable x_s enters basis.
cbar = c(K)' - c(J)' * inv(A(:,J)) * A(:,K)
cbar = cbar'
tmp = findnegative(cbar)
if tmp == -1 % we have found the optimal solution since cbar >= 0
X(J) = xbar;
Y = y;
FVAL = fval;
return
end
s = findnegative(c, K) %x_s enters basis
% vi. Solve system A_J*\bar{a} = a_s. If \bar{a} <= 0, then the problem is
% unbounded.
abar = A(:,J) \ A(:,s)
if findpositive(abar) == -1 % we failed to find positive number
disp('The problem is unbounded.')
return;
end
% vii. Calculate v = \bar{x}_J / \bar{a} and find the smallest rho \in J,
% such that v_rho > 0. Variable x_rho exits basis.
v = xbar ./ abar
rho = J(findpositive(v))
% viii. Update J and K and goto ii.
J = setdiff(J, rho)
J = union(J, s)
K = setdiff(K, s)
K = union(K, rho)
end
Functions findpositive(x) and findnegative(x, S) return the first index of positive or negative value in x. S is the set of indices, over which we look at. If S is omitted, whole vector is checked. Semicolons are omitted for debugging purposes.
The problem I tested this code on is
c = [-3 -1 -3 zeros(1,3)];
A = [2 1 1; 1 2 3; 2 2 1];
A = [A eye(3)];
b = [2; 5; 6];
The reason for zeros(1,3) and eye(3) is that the problem is inequalities and we need slack variables. I have set starting basis to [4 5 6] because the notes say that starting basis should be set to slack variables.
Now, what happens during execution is that on first run of while, variable with index 1 enters basis (in Matlab, indices go from 1 on) and 4 exits it and that is reasonable. On the second run, 2 enters the basis (since it is the smallest index such that c(idx) < 0 and 1 leaves it. But now on the next iteration, 1 enters basis again and I understand why it enters, because it is the smallest index, such that c(idx) < 0. But here the looping starts. I assume that should not have happened, but following the rules I cannot see how to prevent this.
I guess that there has to be something wrong with my interpretation of the notes but I just cannot see where I am wrong. I also remember that when we solved LP on the paper, we were updating our subjective function on each go, since when a variable entered basis, we removed it from the subjective function and expressed that variable in subj. function with the expression from one of the equalities, but I assume that is different algorithm.
Any remarks or help will be highly appreciated.
The problem has been solved. Turned out that the point 7 in the notes was wrong. Instead, point 7 should be
Here is a question about whether we can use vectorization type of operation in matlab to avoid writing for loop.
I have a vector
Q = [0.1,0.3,0.6,1.0]
I generate a uniformly distributed random vector over [0,1)
X = [0.11,0.72,0.32,0.94]
I want to know whether each entry of X is between [0,0.1) or [0.1,0.3) or [0.3,0.6), or [0.6,1.0) and I want to return a vector which contains the index of the maximum element in Q that each entry of X is less than.
I could write a for loop
Y = zeros(length(X),1)
for i = 1:1:length(X)
Y(i) = find(X(i)<Q, 1);
end
Expected result for this example:
Y = [2,4,3,4]
But I wonder if there is a way to avoid writing for loop? (I see many very good answers to my question. Thank you so much! Now if we go one step further, what if my Q is a matrix, such that I want check whether )
Y = zeros(length(X),1)
for i = 1:1:length(X)
Y(i) = find(X(i)<Q(i), 1);
end
Use the second output of max, which acts as a sort of "vectorized find":
[~, Y] = max(bsxfun(#lt, X(:).', Q(:)), [], 1);
How this works:
For each element of X, test if it is less than each element of Q. This is done with bsxfun(#lt, X(:).', Q(:)). Note each column in the result corresponds to an element of X, and each row to an element of Q.
Then, for each element of X, get the index of the first element of Q for which that comparison is true. This is done with [~, Y] = max(..., [], 1). Note that the second output of max returns the index of the first maximizer (along the specified dimension), so in this case it gives the index of the first true in each column.
For your example values,
Q = [0.1, 0.3, 0.6, 1.0];
X = [0.11, 0.72, 0.32, 0.94];
[~, Y] = max(bsxfun(#lt, X(:).', Q(:)), [], 1);
gives
Y =
2 4 3 4
Using bsxfun will help accomplish this. You'll need to read about it. I also added a Q = 0 at the beginning to handle the small X case
X = [0.11,0.72,0.32,0.94 0.01];
Q = [0.1,0.3,0.6,1.0];
Q_extra = [0 Q];
Diff = bsxfun(#minus,X(:)',Q_extra (:)); %vectorized subtraction
logical_matrix = diff(Diff < 0); %find the transition from neg to positive
[X_categories,~] = find(logical_matrix == true); % get indices
% output is 2 4 3 4 1
EDIT: How long does each method take?
I got curious about the difference between each solution:
Test Code Below:
Q = [0,0.1,0.3,0.6,1.0];
X = rand(1,1e3);
tic
Y = zeros(length(X),1);
for i = 1:1:length(X)
Y(i) = find(X(i)<Q, 1);
end
toc
tic
result = arrayfun(#(x)find(x < Q, 1), X);
toc
tic
Q = [0 Q];
Diff = bsxfun(#minus,X(:)',Q(:)); %vectorized subtraction
logical_matrix = diff(Diff < 0); %find the transition from neg to positive
[X_categories,~] = find(logical_matrix == true); % get indices
toc
Run it for yourself, I found that when the size of X was 1e6, bsxfun was much faster, while for smaller arrays the differences were varying and negligible.
SAMPLE: when size X was 1e3
Elapsed time is 0.001582 seconds. % for loop
Elapsed time is 0.007324 seconds. % anonymous function
Elapsed time is 0.000785 seconds. % bsxfun
Octave has a function lookup to do exactly that. It takes a lookup table of sorted values and an array, and returns an array with indices for values in the lookup table.
octave> Q = [0.1 0.3 0.6 1.0];
octave> x = [0.11 0.72 0.32 0.94];
octave> lookup (Q, X)
ans =
1 3 2 3
The only issue is that your lookup table has an implicit zero which be fixed easily with:
octave> lookup ([0 Q], X) # alternatively, just add 1 at the results
ans =
2 4 3 4
You can create an anonymous function to perform the comparison, then apply it to each member of X using arrayfun:
compareFunc = #(x)find(x < Q, 1);
result = arrayfun(compareFunc, X, 'UniformOutput', 1);
The Q array will be stored in the anonymous function ( compareFunc ) when the anonymous function is created.
Or, as one line (Uniform Output is the default behavior of arrayfun):
result = arrayfun(#(x)find(x < Q, 1), X);
Octave does a neat auto-vectorization trick for you if the vectors you have are along different dimensions. If you make Q a column vector, you can do this:
X = [0.11, 0.72, 0.32, 0.94];
Q = [0.1; 0.3; 0.6; 1.0; 2.0; 3.0];
X <= Q
The result is a 6x4 matrix indicating which elements of Q each element of X is less than. I made Q a different length than X just to illustrate this:
0 0 0 0
1 0 0 0
1 0 1 0
1 1 1 1
1 1 1 1
1 1 1 1
Going back to the original example you have, you can do
length(Q) - sum(X <= Q) + 1
to get
2 4 3 4
Notice that I have semicolons instead of commas in the definition of Q. If you want to make it a column vector after defining it, do something like this instead:
length(Q) - sum(X <= Q') + 1
The reason that this works is that Octave implicitly applies bsxfun to an operation on a row and column vector. MATLAB will not do this until R2016b according to #excaza's comment, so in MATLAB you can do this:
length(Q) - sum(bsxfun(#le, X, Q)) + 1
You can play around with this example in IDEOne here.
Inspired by the solution posted by #Mad Physicist, here is my solution.
Q = [0.1,0.3,0.6,1.0]
X = [0.11,0.72,0.32,0.94]
Temp = repmat(X',1,4)<repmat(Q,4,1)
[~, ind]= max( Temp~=0, [], 2 );
The idea is that make the X and Q into the "same shape", then use element wise comparison, then we obtain a logical matrix whose row tells whether a given element in X is less than each of the element in Q, then return the first non-zero index of each row of this logical matrix. I haven't tested how fast this method is comparing to other methods
I am trying to solve this problem:
Write a function called cancel_middle that takes A, an n-by-m
matrix, as an input where both n and m are odd numbers and k, a positive
odd integer that is smaller than both m and n (the function does not have to
check the input). The function returns the input matrix with its center k-by-k
matrix zeroed out.
Check out the following run:
>> cancel_middle(ones(5),3)
ans =
1 1 1 1 1
1 0 0 0 1
1 0 0 0 1
1 0 0 0 1
1 1 1 1 1
My code works only when k=3. How can I generalize it for all odd values of k? Here's what I have so far:
function test(n,m,k)
A = ones(n,m);
B = zeros(k);
A((end+1)/2,(end+1)/2)=B((end+1)/2,(end+1)/2);
A(((end+1)/2)-1,((end+1)/2)-1)= B(1,1);
A(((end+1)/2)-1,((end+1)/2))= B(1,2);
A(((end+1)/2)-1,((end+1)/2)+1)= B(1,3);
A(((end+1)/2),((end+1)/2)-1)= B(2,1);
A(((end+1)/2),((end+1)/2)+1)= B(2,3);
A(((end+1)/2)+1,((end+1)/2)-1)= B(3,1);
A(((end+1)/2)+1,((end+1)/2))= B(3,2);
A((end+1)/2+1,(end+1)/2+1)=B(3,3)
end
You can simplify your code. Please have a look at
Matrix Indexing in MATLAB. "one or both of the row and column subscripts can be vectors", i.e. you can define a submatrix. Then you simply need to do the indexing correct: as you have odd numbers just subtract m-k and n-k and you have the number of elements left from your old matrix A. If you divide it by 2 you get the padding on the left/right, top/bottom. And another +1/-1 because of Matlab indexing.
% Generate test data
n = 13;
m = 11;
A = reshape( 1:m*n, n, m )
k = 3;
% Do the calculations
start_row = (n-k)/2 + 1
start_col = (m-k)/2 + 1
A( start_row:start_row+k-1, start_col:start_col+k-1 ) = zeros( k )
function b = cancel_middle(a,k)
[n,m] = size(a);
start_row = (n-k)/2 + 1;
start_column = (m-k)/2 + 1;
end_row = (n-k)/2 + k;
end_column = (m-k)/2 + k;
a(start_row:end_row,start_column:end_column) = 0;
b = a;
end
I have made a function in an m file called cancel_middle and it basically converts the central k by k matrix as a zero matrix with the same dimensions i.e. k by k.
the rest of the matrix remains the same. It is a general function and you'll need to give 2 inputs i.e the matrix you want to convert and the order of submatrix, which is k.
I need to replace the zeros (or NaNs) in a matrix with the previous element row-wise, so basically I need this Matrix X
[0,1,2,2,1,0;
5,6,3,0,0,2;
0,0,1,1,0,1]
To become like this:
[0,1,2,2,1,1;
5,6,3,3,3,2;
0,0,1,1,1,1],
please note that if the first row element is zero it will stay like that.
I know that this has been solved for a single row or column vector in a vectorized way and this is one of the nicest way of doing that:
id = find(X);
X(id(2:end)) = diff(X(id));
Y = cumsum(X)
The problem is that the indexing of a matrix in Matlab/Octave is consecutive and increments columnwise so it works for a single row or column but the same exact concept cannot be applied but needs to be modified with multiple rows 'cause each of raw/column starts fresh and must be regarded as independent. I've tried my best and googled the whole google but coukldn’t find a way out. If I apply that same very idea in a loop it gets too slow cause my matrices contain 3000 rows at least. Can anyone help me out of this please?
Special case when zeros are isolated in each row
You can do it using the two-output version of find to locate the zeros and NaN's in all columns except the first, and then using linear indexing to fill those entries with their row-wise preceding values:
[ii jj] = find( (X(:,2:end)==0) | isnan(X(:,2:end)) );
X(ii+jj*size(X,1)) = X(ii+(jj-1)*size(X,1));
General case (consecutive zeros are allowed on each row)
X(isnan(X)) = 0; %// handle NaN's and zeros in a unified way
aux = repmat(2.^(1:size(X,2)), size(X,1), 1) .* ...
[ones(size(X,1),1) logical(X(:,2:end))]; %// positive powers of 2 or 0
col = floor(log2(cumsum(aux,2))); %// col index
ind = bsxfun(#plus, (col-1)*size(X,1), (1:size(X,1)).'); %'// linear index
Y = X(ind);
The trick is to make use of the matrix aux, which contains 0 if the corresponding entry of X is 0 and its column number is greater than 1; or else contains 2 raised to the column number. Thus, applying cumsum row-wise to this matrix, taking log2 and rounding down (matrix col) gives the column index of the rightmost nonzero entry up to the current entry, for each row (so this is a kind of row-wise "cummulative max" function.) It only remains to convert from column number to linear index (with bsxfun; could also be done with sub2ind) and use that to index X.
This is valid for moderate sizes of X only. For large sizes, the powers of 2 used by the code quickly approach realmax and incorrect indices result.
Example:
X =
0 1 2 2 1 0 0
5 6 3 0 0 2 3
1 1 1 1 0 1 1
gives
>> Y
Y =
0 1 2 2 1 1 1
5 6 3 3 3 2 3
1 1 1 1 1 1 1
You can generalize your own solution as follows:
Y = X.'; %'// Make a transposed copy of X
Y(isnan(Y)) = 0;
idx = find([ones(1, size(X, 1)); Y(2:end, :)]);
Y(idx(2:end)) = diff(Y(idx));
Y = reshape(cumsum(Y(:)), [], size(X, 1)).'; %'// Reshape back into a matrix
This works by treating the input data as a long vector, applying the original solution and then reshaping the result back into a matrix. The first column is always treated as non-zero so that the values don't propagate throughout rows. Also note that the original matrix is transposed so that it is converted to a vector in row-major order.
Modified version of Eitan's answer to avoid propagating values across rows:
Y = X'; %'
tf = Y > 0;
tf(1,:) = true;
idx = find(tf);
Y(idx(2:end)) = diff(Y(idx));
Y = reshape(cumsum(Y(:)),fliplr(size(X)))';
x=[0,1,2,2,1,0;
5,6,3,0,1,2;
1,1,1,1,0,1];
%Do it column by column is easier
x=x';
rm=0;
while 1
%fields to replace
l=(x==0);
%do nothing for the first row/column
l(1,:)=0;
rm2=sum(sum(l));
if rm2==rm
%nothing to do
break;
else
rm=rm2;
end
%replace zeros
x(l) = x(find(l)-1);
end
x=x';
I have a function I use for a similar problem for filling NaNs. This can probably be cutdown or sped up further - it's extracted from pre-existing code that has a bunch more functionality (forward/backward filling, maximum distance etc).
X = [
0 1 2 2 1 0
5 6 3 0 0 2
1 1 1 1 0 1
0 0 4 5 3 9
];
X(X == 0) = NaN;
Y = nanfill(X,2);
Y(isnan(Y)) = 0
function y = nanfill(x,dim)
if nargin < 2, dim = 1; end
if dim == 2, y = nanfill(x',1)'; return; end
i = find(~isnan(x(:)));
j = 1:size(x,1):numel(x);
j = j(ones(size(x,1),1),:);
ix = max(rep([1; i],diff([1; i; numel(x) + 1])),j(:));
y = reshape(x(ix),size(x));
function y = rep(x,times)
i = find(times);
if length(i) < length(times), x = x(i); times = times(i); end
i = cumsum([1; times(:)]);
j = zeros(i(end)-1,1);
j(i(1:end-1)) = 1;
y = x(cumsum(j));
Say I have the following basic if-statement:
if (A ~= 0)
% do something like divide your favorite number by A
else
% do something like return NaN or infinity
end
The problem is that A is not a simple number but a vector. Matlab returns true if no element in A is 0. What I am looking for is a vectorized? way of perforimg the if-statement above for each element in A.
Actually, I simply want to do this as fast as possible.
Vectorized ifs don't exist, but there are some options. If you want to test for all or any elements true, use the all or any function.
Here's one example of conditionally modifying values of a matrix:
b = A ~= 0; % b is a boolean matrix pointing to nonzero indices
% (b could be derived from some other condition,
% like b = sin(A)>0
A(b) = f(A(b)) % do something with the indices that pass
A(~b) = g(A(~b)) % do something else with the indices that fail
B = zeros(size(A));
B(A~=0) = FAV./A(A~=0);
B(A==0) = NaN;
In general, to perform one operation on some elements of a matrix and another operation on the remaining elements, a one-line solution is:
Z = B .* X + ~B .* Y;
where B is a logical matrix. As an example,
Z = (A == 0) .* -1 + (A ~= 0) .* A;
copies A but assigns -1 everywhere that A is zero.
However, because the question deals with infinity or NaNs, it can be done even more succinctly:
Z = FAV ./ A; % produces inf where A == 0
Z = (A ~= 0) .* FAV ./ A; % produces NaN where A == 0
Are you looking for all non-zero elements? You can do this a couple of ways.
nonzero = find(A); % returns indicies to all non-zero elements of A
y = x./A(nonzero); % divides x by all non-zero elements of A
% y will be the same size as nonzero
Or for a one-liner, you can use a conditional in place of indicies
y = x./A(A~=0); % divides x by all non-zero elements of A
What you need to do is identify the elements you want to operate on. I would use FIND. I store the results in VI (Valid Indicies) and use that to populate the matrix.
clear
clc
den = [2 0 2; 0 2 0; -2 -2 -2]
num = ones(size(den));
frac = nan(size(den));
vi = (den ~=0)
frac(vi) = num(vi)./den(vi)
vi = (den == 0)
frac(vi) = nan %just for good measure...