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0
Not a bug
Wrong display of arrays after vertical concatenation
Hello.
There is a bug in the display of arrays after a vertical concatenation. (Matlab 2014b)
Example:
mp.Digits(34)
a=(1:100)';
b=mp(a(1:50));
c=mp(a(51:100));
d=[b;c];
If you view the variable d in the variable editor it shows the wrong numbers after the 65th entry. If you check it in the command window it is correct.
There is a bug in the display of arrays after a vertical concatenation. (Matlab 2014b)
Example:
mp.Digits(34)
a=(1:100)';
b=mp(a(1:50));
c=mp(a(51:100));
d=[b;c];
If you view the variable d in the variable editor it shows the wrong numbers after the 65th entry. If you check it in the command window it is correct.
0
Answered
meshgrid and ndgrid support
Does the MCT support meshgrid and/or ndgrid in multiprecision? I've been using these in my programs with the mp type, but I wanted to check anyway.
0
Fixed
Output of disp
Hi Pavel,
we have observed the following "bug" (or rather inconsistency) in the formatting of the output via DISP:
Mario Berljafa & Stefan Guettel
we have observed the following "bug" (or rather inconsistency) in the formatting of the output via DISP:
Standard MATLAB code with output:
>> disp(1)
1
MP version:
>> disp(mp(1))
x =
1
There should be no x. As mp(1) pruduces no "x =", the bug must be in disp.
Another inconsistency with MATLAB output formatting concerns format compact. One would expect a reduced amount of space when it is turned on, which appears not to be the case with mp.
>> format compact
>> 1
ans =
1
>> disp(1)
1
>> mp(1)
ans =
1
>> disp(mp(1))
x =
1
Cheers,
0
Answered
Does matrix multiplication use multiple cores?
Hello Pavel.
I was wondering if you updated the software to use multiple cores in case of matrix multiplication. I read something about it in the change log, but it seems to me it still uses 1 core all the time.
Thank you.
Best regards,
Michael
I was wondering if you updated the software to use multiple cores in case of matrix multiplication. I read something about it in the change log, but it seems to me it still uses 1 core all the time.
Thank you.
Best regards,
Michael
0
Answered
Definite integrals with mp-objects
Hello,
Is there a possibility to calculate definite integrals of sum of products of sines/cosines with cosh/sinh with mp-objects?
Kind regards,
Jeremy
0
Answered
Read and write performance in arrays
Hello.
Following example:
Double values:
A=zeros(6000,6000);
tic
for ii = 1:6000
for jj = 1:6000
A(ii,jj)=1.234;
end
end
toc
Elapsed time is 2.038870 seconds
Now without defining A before the for-loop
tic
for ii = 1:6000
for jj= 1:6000
A(ii,jj)=1.234;
end
end
toc
Elapsed time is 120.038870 seconds
Of course the performance is better if I define the array in advance.
Now the same for an mp array with 34 digits:
A=mp(zeros(200,200));
tic
for ii =1:200
for jj = 1:200
A(ii,jj)=mp('1.234');
end
end
toc
Elapsed time is 22.309700 seconds.
Now without defining A before the for-loop
tic
for ii =1:200
for jj = 1:200
A(ii,jj)=mp('1.234');
end
end
toc
Elapsed time is 14.581011 seconds.
How is this possible? This even becomes worse if the matrix is bigger.
Thanks for your answer.
Following example:
Double values:
A=zeros(6000,6000);
tic
for ii = 1:6000
for jj = 1:6000
A(ii,jj)=1.234;
end
end
toc
Elapsed time is 2.038870 seconds
Now without defining A before the for-loop
tic
for ii = 1:6000
for jj= 1:6000
A(ii,jj)=1.234;
end
end
toc
Elapsed time is 120.038870 seconds
Of course the performance is better if I define the array in advance.
Now the same for an mp array with 34 digits:
A=mp(zeros(200,200));
tic
for ii =1:200
for jj = 1:200
A(ii,jj)=mp('1.234');
end
end
toc
Elapsed time is 22.309700 seconds.
Now without defining A before the for-loop
tic
for ii =1:200
for jj = 1:200
A(ii,jj)=mp('1.234');
end
end
toc
Elapsed time is 14.581011 seconds.
How is this possible? This even becomes worse if the matrix is bigger.
Thanks for your answer.
0
Answered
input multiprecision values
Hi, how do I input/read from a file multiprecsion values (calculated elsewhere) into mp objects?
0
Fixed
Inconsistency in handling division by zero
Using double precision:
>> 1/0
ans =
Inf
And :
>> 0^(-1)
ans =
Inf
The results should be consistent using either method as shown above. However, using quad precision, these computations do not give consistent results:
>> 1/mp(0)
ans =
Inf
>> mp(0)^(-1)
ans =
Inf - NaNi
I'm not sure what's under the hood, but it seems to me that either computation should give the same answer.
0
Fixed
error LU function (the devil is in the details)
%% test_lu
clear all %#ok<*CLSCR>
clc
mem = memory %#ok<*NASGU,*NOPTS>
tic, [l,u,p] = lu(9), toc %#ok<*ASGLU>
% now do this...
tic, [l,u,p] = lu(mp(9)), toc
... and look at differences ...
mem = memory
% ... wait for while ( for me about 10..40 sec, randomly !)
% ...
% and then I get out of memory error window
... here is my output ...
mem =
MaxPossibleArrayBytes: 3.3001e+09
MemAvailableAllArrays: 3.3001e+09
MemUsedMATLAB: 735469568
l =
1
u =
9
p =
1
Elapsed time is 0.024654 seconds.
l =
{3x1 cell}
{3x1 cell}
[ 0]
u =
{3x1 cell}
{3x1 cell}
[ 0]
p =
0
Elapsed time is 2.856406 seconds.
mem =
MaxPossibleArrayBytes: 3.2757e+09
MemAvailableAllArrays: 3.2757e+09
MemUsedMATLAB: 760942592
>> mp.Info
-------------------------------------------------------------------------------------------------------------
Multiprecision Computing Toolbox, (c) 2008-2015 Advanpix LLC.
Version : 3.8.4 Build 8915
Platform: 64-bit (Win64)
Release : 2015-07-22
P.S.
I reduced the virtual machine's memory to 1G and get much faster now out of memory
P.S. #2
L = lu(mp(9)) % Matlab crash
clear all %#ok<*CLSCR>
clc
mem = memory %#ok<*NASGU,*NOPTS>
tic, [l,u,p] = lu(9), toc %#ok<*ASGLU>
% now do this...
tic, [l,u,p] = lu(mp(9)), toc
... and look at differences ...
mem = memory
% ... wait for while ( for me about 10..40 sec, randomly !)
% ...
% and then I get out of memory error window
... here is my output ...
mem =
MaxPossibleArrayBytes: 3.3001e+09
MemAvailableAllArrays: 3.3001e+09
MemUsedMATLAB: 735469568
l =
1
u =
9
p =
1
Elapsed time is 0.024654 seconds.
l =
{3x1 cell}
{3x1 cell}
[ 0]
u =
{3x1 cell}
{3x1 cell}
[ 0]
p =
0
Elapsed time is 2.856406 seconds.
mem =
MaxPossibleArrayBytes: 3.2757e+09
MemAvailableAllArrays: 3.2757e+09
MemUsedMATLAB: 760942592
>> mp.Info
-------------------------------------------------------------------------------------------------------------
Multiprecision Computing Toolbox, (c) 2008-2015 Advanpix LLC.
Version : 3.8.4 Build 8915
Platform: 64-bit (Win64)
Release : 2015-07-22
P.S.
I reduced the virtual machine's memory to 1G and get much faster now out of memory
P.S. #2
L = lu(mp(9)) % Matlab crash
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