From 146d391b1d360dae22fe9e3cae60fc8ec6df558d Mon Sep 17 00:00:00 2001 From: Wolfgang Bangerth Date: Tue, 23 Mar 2021 14:26:09 +0100 Subject: [PATCH] Leave a hint in step-7. --- examples/step-7/doc/results.dox | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/examples/step-7/doc/results.dox b/examples/step-7/doc/results.dox index dc51316b05..ee404b7382 100644 --- a/examples/step-7/doc/results.dox +++ b/examples/step-7/doc/results.dox @@ -271,7 +271,7 @@ but it should not be very hard to get the program to work!

Convergence Comparison

-Is Q1 or Q2 better? What about adaptive versus global refinement? A (somewhat +Is $Q_1$ or $Q_2$ better? What about adaptive versus global refinement? A (somewhat unfair but typical) metric to compare them, is to look at the error as a function of the number of unknowns. @@ -288,10 +288,11 @@ work than if we used global refinement. This is not a particularly surprising conclusion, but it's worth checking these sorts of assumptions in practice. -Of course, a fairer comparison would be to plot runtime (switch to release -mode first!) instead of number of unknowns on the $x$ axis. If you -plotted run time against the number of unknowns by timing each -refinement step (e.g., using the Timer class), you will notice that -the linear solver is not perfect -- its run time grows faster than -proportional to the linear system size -- and picking a better -linear solver might be appropriate for this kind of comparison. +Of course, a fairer comparison would be to plot runtime (switch to +release mode first!) instead of number of unknowns on the $x$ axis. If +you plotted run time (check out the Timer class!) against the number +of unknowns by timing each refinement step, you will notice that the +linear system solver we use in this program is not perfect -- its run +time grows faster than proportional to the linear system size -- and +picking a better linear solver might be appropriate for this kind of +comparison. -- 2.39.5