Path: utzoo!utgpu!watserv1!watmath!att!att!linac!pacific.mps.ohio-state.edu!zaphod.mps.ohio-state.edu!wuarchive!cs.utexas.edu!sun-barr!newstop!exodus!hanami.Eng.Sun.COM!landman From: landman@hanami.Eng.Sun.COM (Howard A. Landman) Newsgroups: comp.ai.neural-nets Subject: Re: Backpropagation with Newton's Method, and recurrence. Source code. Message-ID: <3146@exodus.Eng.Sun.COM> Date: 20 Nov 90 01:49:39 GMT References: <7607@uwm.edu> <1248@helens.Stanford.EDU> <7684@uwm.edu> Sender: news@exodus.Eng.Sun.COM Organization: Sun Microsystems, Mt. View, Ca. Lines: 12 >In article <1248@helens.Stanford.EDU> wan@isl.Stanford.EDU (Eric A. Wan) writes: >>You mention the method does not work well for f(x) = x^2. In fact, Newton's >>Method applied to the gradient of x^2 converges in one step. In article <7684@uwm.edu> markh@csd4.csd.uwm.edu (Mark William Hopkins) writes: >You lost me here. Try using Newton's method to find a zero of x^2. The gradient of x^2 is 2x. -- Howard A. Landman landman@eng.sun.com -or- sun!landman