Artour
M&G Limited
Followers: 0 Following: 0
Hello,
I have a problem with covariance matrices that turn non-symmetric and negative definite as a consequence of rounding off errors. I've symmetrized the matrices by
A = (A + A')/2;
However, I haven't been able to correct the negative eigenvalues, and it's very important that the matrices are always non-negative definite. For example, the following method doesn't help:
[V,D] = eig(A);
D = max(D,0);
A = V*D/V;
Can anyone please suggest what can be done here.
Thanks,
Artour
Statistics
RANK
175,143
of 301,151
REPUTATION
0
CONTRIBUTIONS
1 Question
1 Answer
ANSWER ACCEPTANCE
100.0%
VOTES RECEIVED
0
RANK
of 21,181
REPUTATION
N/A
AVERAGE RATING
0.00
CONTRIBUTIONS
0 Files
DOWNLOADS
0
ALL TIME DOWNLOADS
0
RANK
of 173,007
CONTRIBUTIONS
0 Problems
0 Solutions
SCORE
0
NUMBER OF BADGES
0
CONTRIBUTIONS
0 Posts
CONTRIBUTIONS
0 Public Channels
AVERAGE RATING
CONTRIBUTIONS
0 Discussions
AVERAGE NO. OF LIKES
Feeds
Non-symmetric and negative definite covariance matrices, as a consequence of rounding off errors
Matt J, thanks for your answer. Unfortunately I can't make the matrix positive definite. I'm looking at a Kalman filtering probl...
12 years ago | 0
Question
Non-symmetric and negative definite covariance matrices, as a consequence of rounding off errors
Hello, I have a problem with covariance matrices that turn non-symmetric and negative definite, as a consequence of rounding ...
12 years ago | 2 answers | 0
