I thought you needed to solve an overdermined system, for which an inverse matrix does not exist. If you has reduced it to a square matrix, you can use faster methods to solve it, but even that you do not need to compute an inverse matrix to obtain a solution to a linear system. Rgds, Andrew > Date: Thu, 20 May 2004 09:53:14 -0700 (PDT) > From: Rahul Parthasarthy <> > Subject: Re: Re: Estimate LPC's > > Hello, > > Thank you for your help and support. > > I found the right solution to this problem in one Signal Processing book. > This problem is solved in book entitled 'Optimal and Adaptive Signal > Processing' by Peter M. Clarkson (in Chapter 2). They have derived an > expression to solve these kind of eaquations (Ax=B). I have developed C > program to solve Ax=B, but I haven't used QR Decomposition, I have used > simple matrix inversion technique. > > Once again, thank you for for you help and support. > > Regards, > Rahul Parthasarthy. > > Andrew Nesterov <> wrote: > > A solution to an overdermined linear system can be found > by either SVD or QR decomposition methods. They are stored > somewhere on the net. E.g. CLAPACK on http://netlib.org. > > We have a commercial solution for C6000 and TS. Please > contact me if you need more information. > > Rgds, > Andrew > > -- > Andrew V. Nesterov () > Optimized TMS320C6000 DSP Software > http://microprocessing.iwarp.com > > > Date: Mon, 17 May 2004 23:14:57 -0000 > > From: "rahulparthasarthy" <> > > Subject: Estimate LPC's > > > > Hello, > > > > I am trying to compute a solution using the least- square- error > > principle to an overdetermined system of equations. > > > > The equation is as below: > > > > Error = summation (y[n] ? summation (ai x[n-i] ) )... in 'ai' > > term `i' is subscript > > > > where `n' ranges from 1 to 300 and `i' ranges from 1 to 18. The > > outside summation is for `n' (1,2,3,4,?299,300) whereas the inside > > summation is for 'i' (1,2,3,4?18) > > > > I am interested in calculating `ai ` ( i is subscript) where 'ai' > > represents Linear Prediction Coefficients. I could have used the > > Levinson-Durbin algorithm but in this case, two different signals are > > in consideration, namely, y[n] and x[n], and hence > > the `autocorrelation' method cannot be used. I have y[n] and x[n] > > available but not able to evaluate that expression. > > > > I would be obliged if anybody could help me in solving/evaluating > > the above equation so that I can find ai's (LP coefficients). Is it > > possible to solve using Maple, Matlab, Mathematica etc? I am > > interested in developing a C program for the above expression. > > > > Regards, > > Rahul Parthasarthy > > > > > |