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27 <a name="math_toolkit.bad_roots"></a><a class="link" href="bad_roots.html" title="Examples Where Root Finding Goes Wrong">Examples Where Root Finding Goes
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31 There are many reasons why root root finding can fail, here are just a few
32 of the more common examples:
35 <a name="math_toolkit.bad_roots.h0"></a>
36 <span class="phrase"><a name="math_toolkit.bad_roots.local_minima"></a></span><a class="link" href="bad_roots.html#math_toolkit.bad_roots.local_minima">Local
40 If you start in the wrong place, such as z<sub>0</sub> here:
43 <span class="inlinemediaobject"><object type="image/svg+xml" data="../../roots/bad_root_1.svg" width="372" height="262"></object></span>
46 Then almost any root-finding algorithm will descend into a local minima rather
50 <a name="math_toolkit.bad_roots.h1"></a>
51 <span class="phrase"><a name="math_toolkit.bad_roots.flatlining"></a></span><a class="link" href="bad_roots.html#math_toolkit.bad_roots.flatlining">Flatlining</a>
54 In this example, we're starting from a location (z<sub>0</sub>) where the first derivative
58 <span class="inlinemediaobject"><object type="image/svg+xml" data="../../roots/bad_root_2.svg" width="372" height="262"></object></span>
61 In this situation the next iteration will shoot off to infinity (assuming we're
62 using derivatives that is). Our code guards against this by insisting that
63 the root is always bracketed, and then never stepping outside those bounds.
64 In a case like this, no root finding algorithm can do better than bisecting
65 until the root is found.
68 Note that there is no scale on the graph, we have seen examples of this situation
69 occur in practice <span class="emphasis"><em>even when several decimal places of the initial
70 guess z<sub>0</sub> are correct.</em></span>
73 This is really a special case of a more common situation where root finding
74 with derivatives is <span class="emphasis"><em>divergent</em></span>. Consider starting at z<sub>0</sub> in
78 <span class="inlinemediaobject"><object type="image/svg+xml" data="../../roots/bad_root_4.svg" width="372" height="262"></object></span>
81 An initial Newton step would take you further from the root than you started,
82 as will all subsequent steps.
85 <a name="math_toolkit.bad_roots.h2"></a>
86 <span class="phrase"><a name="math_toolkit.bad_roots.micro_stepping_non_convergence"></a></span><a class="link" href="bad_roots.html#math_toolkit.bad_roots.micro_stepping_non_convergence">Micro-stepping
90 Consider starting at z<sub>0</sub> in this situation:
93 <span class="inlinemediaobject"><object type="image/svg+xml" data="../../roots/bad_root_3.svg" width="372" height="262"></object></span>
96 The first derivative is essentially infinite, and the second close to zero
97 (and so offers no correction if we use it), as a result we take a very small
98 first step. In the worst case situation, the first step is so small - perhaps
99 even so small that subtracting from z<sub>0</sub> has no effect at the current working
100 precision - that our algorithm will assume we are at the root already and terminate.
101 Otherwise we will take lot's of very small steps which never converge on the
102 root: our algorithms will protect against that by reverting to bisection.
105 An example of this situation would be trying to find the root of e<sup>-1/z<sup>2</sup></sup> - this
106 function has a single root at <span class="emphasis"><em>z = 0</em></span>, but for <span class="emphasis"><em>z<sub>0</sub> <
107 0</em></span> neither Newton nor Halley steps will ever converge on the root,
108 and for <span class="emphasis"><em>z<sub>0</sub> > 0</em></span> the steps are actually divergent.
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114 Agrawal, Anton Bikineev, Paul A. Bristow, Marco Guazzone, Christopher Kormanyos,
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116 Råde, Gautam Sewani, Benjamin Sobotta, Nicholas Thompson, Thijs van den Berg,
117 Daryle Walker and Xiaogang Zhang<p>
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