Hi, have a look at scipy optimize. For a solution with only positive values you could consider using scipy.optimize.nnls, if you want more general (linear) constraints, have a look at the linear programming functions.
Another possibility would be looking at openOpt, which has probably more general solvers. On Mar 9, 2012, at 7:33, salahudeen razac wrote: > > I wrote a script to solve the equation ‘P =Kd .V2 + Kl.VA.BT + C’ . To solve > the equation I have used the matrix method. > > > Ie, K*X = P where > > [P] = [ P1 > > P2 > a column matrix with the total powers P1, P2,P3…….PN at > (V1,T1),(V2,T2)……(VN.TN) respectively,\ > > P3 > > .. > > PN ] > > > > [K] = [ V12 V1A.BT1 1 > > V22 V2A.BT2 > 1 > > V32 V3A.BT3 > 1 > > ……………………….. > > VN2 VNA.BTN > 1 ] > > > > [X]= [ Kd A > column matrix with the unknowns > > Kl > > C ] > > > Now in order to solve multiply both sides with KT, ie KT.K.X = > KTP or A.X = B where A= KT.K and B= KTP > > > > Now we will get the matrix X by using linalg.solve(A,B) function which will > eventually solve for a set of linear equations of the form Ax=B > > > > I was able to get the solution. But it is not acceptable since some of the > values are negative. SO I want to know is there any way to give some > constraints like the solution should contain only positive values and give > any range for the solution? > > > > I also tried with linalg.lstsq and I am getting the same results > > ----- > Salahudeen razak | +917760902602 > > o__ > _> /__ > (_) \(_)... Burn fat not fuel > > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion
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