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<DIV><FONT color=#0000ff size=2 face=Verdana>hello, petsc teams,</FONT></DIV>
<DIV><FONT color=#0000ff></FONT> </DIV>
<DIV><FONT color=#0000ff>If I want to
use finite difference jacobian approximations,how should I
define </FONT></DIV>
<DIV><FONT color=#0000ff></FONT> </DIV>
<DIV><FONT color=#0000ff>the function FormJacobian()? I don't get analytic
jacobians.</FONT></DIV>
<DIV><FONT color=#0000ff></FONT> </DIV>
<DIV><FONT color=#0000ff>thanks,</FONT></DIV>
<DIV><FONT size=2 face=Verdana></FONT><FONT color=#0000ff>leping</FONT></DIV>
<DIV><FONT size=2 face=Verdana></FONT><FONT color=#0000ff></FONT> </DIV>
<DIV><FONT color=#c0c0c0 size=2 face=Verdana><FONT
color=#0000ff>2010-05-02</FONT> </FONT></DIV><FONT color=#000080 size=2
face=Verdana>
<HR style="WIDTH: 100px" align=left color=#b5c4df SIZE=1>
</FONT>
<DIV><FONT color=#c0c0c0 size=2 face=Verdana>
<DIV></FONT>
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</DIV></DIV>
<DIV><FONT size=2 face=Verdana><STRONG>发件人：</STRONG> Jed Brown </FONT></DIV>
<DIV><FONT size=2 face=Verdana><STRONG>发送时间：</STRONG> 2010-05-02 20:10:20
</FONT></DIV>
<DIV><FONT size=2 face=Verdana><STRONG>收件人：</STRONG> chenleping; PETSc users
list </FONT></DIV>
<DIV><FONT size=2 face=Verdana><STRONG>抄送：</STRONG> </FONT></DIV>
<DIV><FONT size=2 face=Verdana><STRONG>主题：</STRONG> Re: [petsc-users] about
finite difference jacobian approximations </FONT></DIV>
<DIV><FONT size=2 face=Verdana></FONT> </DIV>
<DIV><FONT size=2 face=Verdana>
<DIV>On Sat, 1 May 2010 15:35:25 +0800, "=?gb2312?B?s8LA1sa9o6hMZXBpbmcgQ2hlbqOp?=" <chenleping@yahoo.cn> wrote:</DIV>
<DIV>> hello, petsc teams,</DIV>
<DIV>> </DIV>
<DIV>> When I use finite difference jacobian approximations, I only find the</DIV>
<DIV>> computation methods and results of jacobian matrix cannot influence</DIV>
<DIV>> iterative process of snes.</DIV>
<DIV></DIV>
<DIV>I don't know what you're asking. The accuracy of the Jacobian certainly</DIV>
<DIV>can affect the convergence rate; for simple or well-conditioned</DIV>
<DIV>problems, finite difference Jacobians are usually reliable. Analytic</DIV>
<DIV>Jacobians, or Jacobians from automatic differentiation (AD) are usually</DIV>
<DIV>better when available and not too expensive to compute. Another common</DIV>
<DIV>thing is to assemble a "cheap" preconditioning matrix (dropping terms,</DIV>
<DIV>or with a lower order discretization) for use with a matrix-free</DIV>
<DIV>(usually finite difference or AD) operator.</DIV>
<DIV></DIV>
<DIV>Jed</DIV></FONT></DIV></BODY></HTML>