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Tim

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13 May 2013 LBFGSB (L-BFGS-B) mex wrapper Mex wrapper for lbfgsb v3.0 fortan library. L-bfgs-b solves box-constrained optimization. Author: Stephen Becker

My apologies for the numerous replies. I had to manually update dcopy dscal and daxpy as you suggested. The mex file is generated ("lbfgsb_wrapper.mexmaci64") but it crashes matlab. The error report says: "I should have added that the error report listed "Symbol not found: __gfortran_transfer_real_write""

13 May 2013 LBFGSB (L-BFGS-B) mex wrapper Mex wrapper for lbfgsb v3.0 fortan library. L-bfgs-b solves box-constrained optimization. Author: Stephen Becker

I should have added that the error report listed "Symbol not found: __gfortran_transfer_real_write"

13 May 2013 LBFGSB (L-BFGS-B) mex wrapper Mex wrapper for lbfgsb v3.0 fortan library. L-bfgs-b solves box-constrained optimization. Author: Stephen Becker

Hi,
Thank you for writing this code. I hope to be able to use it, but I am having some compilation problems. I am running a 64 bit mac, OS 10.6.8, running Matlab v. R2010b.

I get the error "script failed, try running script by hand perhaps?," though the files are still generated with ddot32 replacing dot. It does not, however, compile when using the subsequent mex command ("can't find "_daxpy_" etc). It did compile using the previous, commented out mex command using lmwblas, and generated a mexmaci64 file. If I try running the example code calling lfbgs, Matlab crashes.

The libblas version is 10.2.3 for R2010b. If you could offer any solutions I would be grateful,

07 Feb 2013 Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering. Author: Michael Kleder

07 Feb 2013 Learning the Kalman Filter Basic Kalman filter, heavily commented, for beginners to Kalman filtering. Author: Michael Kleder

I am new to k. filtering, and I don't understand the following: the covariance matrix P appears to be only a function of the following inputs: A (defined by the system), P (itself), Q (known p.noise cov.), and H (typically identity), and also the Kalman gain K. K itself is a function only of P, H, and R (known m.noise cov.).

None of these are re-defined during iteration except P. How then is the estimate of the covariance matrix P tied to observations z? The result of this appears to be that the Kalman gain explodes by the third or fourth iteration, making the output mirror the noisy input. This seems to be what Enver Bahar is noticing too, I think.

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