[math] Using optimization package for non linear equations

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[math] Using optimization package for non linear equations

 I've read optimization guide (http://commons.apache.org/proper/commons-math/userguide/optimization.html) yet I'm still unsure about how to apply optimization examples in my case. I have a set of nonlinear equations in the form: e^(qt)S - e^(rt)K1 = b1 e^(qt)S - e^(rt)K2 = b2 ... e^(qt)S - e^(rt)Kn = bn r and q are unknown variables (parameters?), while S, t, K1...Kn are known scalars. b1...bn is a set of known scalar observations that could contain errors. Since r,q are two unknown variables I could simply solve the set of two equations b1,b2 and find the desired q and r. However, those two particular observations b1 and b2 could contain the most error in observation set data thus propagating the error to r and q. How could I use optimization package to find optimal r and q for the entire set of observations that would minimize the sum of the difference? Regards, Vladimir
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Re: [math] Using optimization package for non linear equations

 Hi Vladimir. On Thu, 28 Sep 2017 09:37:15 -0400, Vladimir Blagojevic wrote: > I've read optimization guide (http://commons.apache.org/pro> per/commons-math/userguide/optimization.html) yet I'm still unsure > about how > to apply optimization examples in my case. The unit tests provide working examples of how to use the library, e.g.:     https://git1-us-west.apache.org/repos/asf?p=commons-math.git;a=blob;f=src/test/java/org/apache/commons/math4/fitting/leastsquares/StraightLineProblem.javashould help you get started. HTH, Gilles > I have a set of nonlinear equations in the form: > > e^(qt)S - e^(rt)K1 = b1 > e^(qt)S - e^(rt)K2 = b2 > ... > e^(qt)S - e^(rt)Kn = bn > > r and q are unknown variables (parameters?), while S, t, K1...Kn are > known > scalars. b1...bn is a set of known scalar observations that could > contain > errors. > > Since r,q are two unknown variables I could simply solve the set of > two > equations b1,b2 and find the desired q and r. However, those two > particular > observations b1 and b2 could contain the most error in observation > set data > thus propagating the error to r and q. > > How could I use optimization package to find optimal r and q for the > entire > set of observations that would minimize the sum of the difference? > > Regards, > Vladimir --------------------------------------------------------------------- To unsubscribe, e-mail: [hidden email] For additional commands, e-mail: [hidden email]