<HTML><HEAD></HEAD>
<BODY dir=ltr>
<DIV dir=ltr>
<DIV style="FONT-SIZE: 12pt; FONT-FAMILY: 'Calibri'; COLOR: #000000">
<DIV>1. Yes, you’re correct that NLP function evaluations means objective,
gradient, Jacobian, and Hessian functions. These are considered user-defined
functions, although when using the AMPL interface to Ipopt it is that AMPL
interface code that is responsible for evaluating these functions. The
inside-Ipopt time is primarily the time spent in the linear solver (ma27 in your
example data), solving the KKT system to determine the Newton step at each
iteration, potentially multiple times per iteration if Hessian regularization is
required, then a bit more cheap math for the line search. You can get a more
detailed breakdown of where the time is being spent by setting the option
print_timing_statistics to yes.</DIV>
<DIV> </DIV>
<DIV>2. Conceptually these should be parallelizable since it’s just function
evaluation and each element of the vector and sparse matrix quantities only
depends on the current primal-dual evaluation point, not the other vector/matrix
elements. Since you’re using the AMPL interface I’m not sure how easy this is to
implement in practice. I don’t believe the AMPL interface code is written in a
way that could be easily parallelized, but I could be wrong here. How
complicated would your problem be to implement in C++ or something you have more
control over? I believe some of the auto-differentiation tools should support
some form of parallel evaluation?</DIV>
<DIV> </DIV>
<DIV>I find it a bit surprising to see an AMPL model taking almost 4 times
longer in the function evaluations than the linear solver, I usually expect the
linear solver time to dominate for most problems (unless you’re using an
interface like Matlab or Python where the user functions might be written as
slow interpreted code). Does your problem have a large number of complicated
nested nonlinearities or discontinuities or something else that may be giving
AMPL trouble? Look at the more detailed timing statistics to see which of the
functions is taking most of the time, if the Hessian is taking an inordinate
amount of time you could try the quasi-Newton limited-memory Hessian option by
setting the hessian_approximation option to limited-memory. Otherwise is there
any type of reformulation you could do, potentially introducing more variables
but making the problem sparser or consolidating some of the more difficult
nonlinearities into fewer variables or constraint elements?</DIV>
<DIV> </DIV>
<DIV>-Tony</DIV>
<DIV> </DIV>
<DIV
style="FONT-SIZE: small; FONT-FAMILY: 'Calibri'; FONT-WEIGHT: normal; COLOR: #000000; FONT-STYLE: normal; TEXT-DECORATION: none; DISPLAY: inline">
<DIV style="FONT: 10pt tahoma">
<DIV> </DIV>
<DIV style="BACKGROUND: #f5f5f5">
<DIV style="font-color: black"><B>From:</B> <A title=xieyao04@gmail.com
href="mailto:xieyao04@gmail.com">Yao Xie</A> </DIV>
<DIV><B>Sent:</B> Tuesday, October 01, 2013 1:43 PM</DIV>
<DIV><B>To:</B> <A title=ipopt@list.coin-or.org
href="mailto:ipopt@list.coin-or.org">ipopt@list.coin-or.org</A> </DIV>
<DIV><B>Subject:</B> [Ipopt] Questions on "Total CPU secs in NLP function
evaluations"</DIV></DIV></DIV>
<DIV> </DIV></DIV>
<DIV
style="FONT-SIZE: small; FONT-FAMILY: 'Calibri'; FONT-WEIGHT: normal; COLOR: #000000; FONT-STYLE: normal; TEXT-DECORATION: none; DISPLAY: inline">
<DIV dir=ltr>Hi all,
<DIV> </DIV>
<DIV>I couldn't find documentation to explain the difference of "Total CPU secs
in IPOPT (w/o function evaluations)" and "Total CPU secs in NLP function
evaluations". I was using AMPL interface and got a very long function evaluation
time (details below).</DIV>
<DIV> </DIV>
<DIV>My questions are:</DIV>
<DIV>1) What's the difference between these two CPU time? Does "NLP function
evaluations" mean to evaluate objective function values, Jacobian, Hessian
matrix? Which parts are in IPOPT and which are outside IPOPT?</DIV>
<DIV>2) Is there any way to speed up this NLP function evaluation part? It's
just evaluation, can we parallelize it? Thanks!</DIV>
<DIV> </DIV>
<DIV> </DIV></DIV></DIV></DIV></DIV></BODY></HTML>