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<span id="Solvers"></span><div class="header">
<p>
Next: <a href="Minimizers.html" accesskey="n" rel="next">Minimizers</a>, Up: <a href="Nonlinear-Equations.html" accesskey="u" rel="up">Nonlinear Equations</a> [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html" title="Index" rel="index">Index</a>]</p>
</div>
<hr>
<span id="Solvers-1"></span><h3 class="section">20.1 Solvers</h3>
<p>Octave can solve sets of nonlinear equations of the form
</p>
<div class="example">
<pre class="example">F (x) = 0
</pre></div>
<p>using the function <code>fsolve</code>, which is based on the <small>MINPACK</small>
subroutine <code>hybrd</code>. This is an iterative technique so a starting
point must be provided. This also has the consequence that
convergence is not guaranteed even if a solution exists.
</p>
<span id="XREFfsolve"></span><dl>
<dt id="index-fsolve">: <em></em> <strong>fsolve</strong> <em>(<var>fcn</var>, <var>x0</var>)</em></dt>
<dt id="index-fsolve-1">: <em></em> <strong>fsolve</strong> <em>(<var>fcn</var>, <var>x0</var>, <var>options</var>)</em></dt>
<dt id="index-fsolve-2">: <em>[<var>x</var>, <var>fval</var>, <var>info</var>, <var>output</var>, <var>fjac</var>] =</em> <strong>fsolve</strong> <em>(…)</em></dt>
<dd><p>Solve a system of nonlinear equations defined by the function <var>fcn</var>.
</p>
<p><var>fun</var> is a function handle, inline function, or string containing the
name of the function to evaluate. <var>fcn</var> should accept a vector (array)
defining the unknown variables, and return a vector of left-hand sides of
the equations. Right-hand sides are defined to be zeros. In other words,
this function attempts to determine a vector <var>x</var> such that
<code><var>fcn</var> (<var>x</var>)</code> gives (approximately) all zeros.
</p>
<p><var>x0</var> is an initial guess for the solution. The shape of <var>x0</var> is
preserved in all calls to <var>fcn</var>, but otherwise is treated as a column
vector.
</p>
<p><var>options</var> is a structure specifying additional parameters which
control the algorithm. Currently, <code>fsolve</code> recognizes these options:
<code>"AutoScaling"</code>, <code>"ComplexEqn"</code>, <code>"FinDiffType"</code>,
<code>"FunValCheck"</code>, <code>"Jacobian"</code>, <code>"MaxFunEvals"</code>,
<code>"MaxIter"</code>, <code>"OutputFcn"</code>, <code>"TolFun"</code>, <code>"TolX"</code>,
<code>"TypicalX"</code>, and <code>"Updating"</code>.
</p>
<p>If <code>"AutoScaling"</code> is <code>"on"</code>, the variables will be
automatically scaled according to the column norms of the (estimated)
Jacobian. As a result, <code>"TolFun"</code> becomes scaling-independent. By
default, this option is <code>"off"</code> because it may sometimes deliver
unexpected (though mathematically correct) results.
</p>
<p>If <code>"ComplexEqn"</code> is <code>"on"</code>, <code>fsolve</code> will attempt to solve
complex equations in complex variables, assuming that the equations possess
a complex derivative (i.e., are holomorphic). If this is not what you want,
you should unpack the real and imaginary parts of the system to get a real
system.
</p>
<p>If <code>"Jacobian"</code> is <code>"on"</code>, it specifies that <var>fcn</var>—when
called with 2 output arguments—also returns the Jacobian matrix of
right-hand sides at the requested point.
</p>
<p><code>"MaxFunEvals"</code> proscribes the maximum number of function evaluations
before optimization is halted. The default value is
<code>100 * number_of_variables</code>, i.e., <code>100 * length (<var>x0</var>)</code>.
The value must be a positive integer.
</p>
<p>If <code>"Updating"</code> is <code>"on"</code>, the function will attempt to use
Broyden updates to update the Jacobian, in order to reduce the
number of Jacobian calculations. If your user function always calculates
the Jacobian (regardless of number of output arguments) then this option
provides no advantage and should be disabled.
</p>
<p><code>"TolX"</code> specifies the termination tolerance in the unknown variables,
while <code>"TolFun"</code> is a tolerance for equations. Default is <code>1e-6</code>
for both <code>"TolX"</code> and <code>"TolFun"</code>.
</p>
<p>For a description of the other options, see <code>optimset</code>. To initialize
an options structure with default values for <code>fsolve</code> use
<code>options = optimset ("fsolve")</code>.
</p>
<p>The first output <var>x</var> is the solution while the second output <var>fval</var>
contains the value of the function <var>fcn</var> evaluated at <var>x</var> (ideally
a vector of all zeros).
</p>
<p>The third output <var>info</var> reports whether the algorithm succeeded and may
take one of the following values:
</p>
<dl compact="compact">
<dt>1</dt>
<dd><p>Converged to a solution point. Relative residual error is less than
specified by <code>TolFun</code>.
</p>
</dd>
<dt>2</dt>
<dd><p>Last relative step size was less than <code>TolX</code>.
</p>
</dd>
<dt>3</dt>
<dd><p>Last relative decrease in residual was less than <code>TolFun</code>.
</p>
</dd>
<dt>0</dt>
<dd><p>Iteration limit (either <code>MaxIter</code> or <code>MaxFunEvals</code>) exceeded.
</p>
</dd>
<dt>-1</dt>
<dd><p>Stopped by <code>OutputFcn</code>.
</p>
</dd>
<dt>-3</dt>
<dd><p>The trust region radius became excessively small.
</p></dd>
</dl>
<p><var>output</var> is a structure containing runtime information about the
<code>fsolve</code> algorithm. Fields in the structure are:
</p>
<dl compact="compact">
<dt><code>iterations</code></dt>
<dd><p>Number of iterations through loop.
</p>
</dd>
<dt><code>successful</code></dt>
<dd><p>Number of successful iterations.
</p>
</dd>
<dt><code>funcCount</code></dt>
<dd><p>Number of function evaluations.
</p>
</dd>
</dl>
<p>The final output <var>fjac</var> contains the value of the Jacobian evaluated
at <var>x</var>.
</p>
<p>Note: If you only have a single nonlinear equation of one variable, using
<code>fzero</code> is usually a much better idea.
</p>
<p>Note about user-supplied Jacobians:
As an inherent property of the algorithm, a Jacobian is always requested for
a solution vector whose residual vector is already known, and it is the last
accepted successful step. Often this will be one of the last two calls, but
not always. If the savings by reusing intermediate results from residual
calculation in Jacobian calculation are significant, the best strategy is to
employ <code>OutputFcn</code>: After a vector is evaluated for residuals, if
<code>OutputFcn</code> is called with that vector, then the intermediate results
should be saved for future Jacobian evaluation, and should be kept until a
Jacobian evaluation is requested or until <code>OutputFcn</code> is called with a
different vector, in which case they should be dropped in favor of this most
recent vector. A short example how this can be achieved follows:
</p>
<div class="example">
<pre class="example">function [fval, fjac] = user_func (x, optimvalues, state)
persistent sav = [], sav0 = [];
if (nargin == 1)
## evaluation call
if (nargout == 1)
sav0.x = x; # mark saved vector
## calculate fval, save results to sav0.
elseif (nargout == 2)
## calculate fjac using sav.
endif
else
## outputfcn call.
if (all (x == sav0.x))
sav = sav0;
endif
## maybe output iteration status, etc.
endif
endfunction
## …
fsolve (@user_func, x0, optimset ("OutputFcn", @user_func, …))
</pre></div>
<p><strong>See also:</strong> <a href="#XREFfzero">fzero</a>, <a href="Linear-Least-Squares.html#XREFoptimset">optimset</a>.
</p></dd></dl>
<p>The following is a complete example. To solve the set of equations
</p>
<div class="example">
<pre class="example">-2x^2 + 3xy + 4 sin(y) = 6
3x^2 - 2xy^2 + 3 cos(x) = -4
</pre></div>
<p>you first need to write a function to compute the value of the given
function. For example:
</p>
<div class="example">
<pre class="example">function y = f (x)
y = zeros (2, 1);
y(1) = -2*x(1)^2 + 3*x(1)*x(2) + 4*sin(x(2)) - 6;
y(2) = 3*x(1)^2 - 2*x(1)*x(2)^2 + 3*cos(x(1)) + 4;
endfunction
</pre></div>
<p>Then, call <code>fsolve</code> with a specified initial condition to find the
roots of the system of equations. For example, given the function
<code>f</code> defined above,
</p>
<div class="example">
<pre class="example">[x, fval, info] = fsolve (@f, [1; 2])
</pre></div>
<p>results in the solution
</p>
<div class="example">
<pre class="example">x =
0.57983
2.54621
fval =
-5.7184e-10
5.5460e-10
info = 1
</pre></div>
<p>A value of <code>info = 1</code> indicates that the solution has converged.
</p>
<p>When no Jacobian is supplied (as in the example above) it is approximated
numerically. This requires more function evaluations, and hence is
less efficient. In the example above we could compute the Jacobian
analytically as
</p>
<div class="example">
<pre class="example">function [y, jac] = f (x)
y = zeros (2, 1);
y(1) = -2*x(1)^2 + 3*x(1)*x(2) + 4*sin(x(2)) - 6;
y(2) = 3*x(1)^2 - 2*x(1)*x(2)^2 + 3*cos(x(1)) + 4;
if (nargout == 2)
jac = zeros (2, 2);
jac(1,1) = 3*x(2) - 4*x(1);
jac(1,2) = 4*cos(x(2)) + 3*x(1);
jac(2,1) = -2*x(2)^2 - 3*sin(x(1)) + 6*x(1);
jac(2,2) = -4*x(1)*x(2);
endif
endfunction
</pre></div>
<p>The Jacobian can then be used with the following call to <code>fsolve</code>:
</p>
<div class="example">
<pre class="example">[x, fval, info] = fsolve (@f, [1; 2], optimset ("jacobian", "on"));
</pre></div>
<p>which gives the same solution as before.
</p>
<span id="XREFfzero"></span><dl>
<dt id="index-fzero">: <em></em> <strong>fzero</strong> <em>(<var>fun</var>, <var>x0</var>)</em></dt>
<dt id="index-fzero-1">: <em></em> <strong>fzero</strong> <em>(<var>fun</var>, <var>x0</var>, <var>options</var>)</em></dt>
<dt id="index-fzero-2">: <em>[<var>x</var>, <var>fval</var>, <var>info</var>, <var>output</var>] =</em> <strong>fzero</strong> <em>(…)</em></dt>
<dd><p>Find a zero of a univariate function.
</p>
<p><var>fun</var> is a function handle, inline function, or string containing the
name of the function to evaluate.
</p>
<p><var>x0</var> should be a two-element vector specifying two points which
bracket a zero. In other words, there must be a change in sign of the
function between <var>x0</var>(1) and <var>x0</var>(2). More mathematically, the
following must hold
</p>
<div class="example">
<pre class="example">sign (<var>fun</var>(<var>x0</var>(1))) * sign (<var>fun</var>(<var>x0</var>(2))) <= 0
</pre></div>
<p>If <var>x0</var> is a single scalar then several nearby and distant values are
probed in an attempt to obtain a valid bracketing. If this is not
successful, the function fails.
</p>
<p><var>options</var> is a structure specifying additional options. Currently,
<code>fzero</code> recognizes these options:
<code>"Display"</code>, <code>"FunValCheck"</code>, <code>"MaxFunEvals"</code>,
<code>"MaxIter"</code>, <code>"OutputFcn"</code>, and <code>"TolX"</code>.
</p>
<p><code>"MaxFunEvals"</code> proscribes the maximum number of function evaluations
before the search is halted. The default value is <code>Inf</code>.
The value must be a positive integer.
</p>
<p><code>"MaxIter"</code> proscribes the maximum number of algorithm iterations
before the search is halted. The default value is <code>Inf</code>.
The value must be a positive integer.
</p>
<p><code>"TolX"</code> specifies the termination tolerance for the solution <var>x</var>.
The default value is <code>eps</code>.
</p>
<p>For a description of the other options, see <a href="Linear-Least-Squares.html#XREFoptimset">optimset</a>.
To initialize an options structure with default values for <code>fzero</code> use
<code>options = optimset ("fzero")</code>.
</p>
<p>On exit, the function returns <var>x</var>, the approximate zero point, and
<var>fval</var>, the function evaluated at <var>x</var>.
</p>
<p>The third output <var>info</var> reports whether the algorithm succeeded and
may take one of the following values:
</p>
<ul>
<li> 1
The algorithm converged to a solution.
</li><li> 0
Maximum number of iterations or function evaluations has been reached.
</li><li> -1
The algorithm has been terminated by a user <code>OutputFcn</code>.
</li><li> -5
The algorithm may have converged to a singular point.
</li></ul>
<p><var>output</var> is a structure containing runtime information about the
<code>fzero</code> algorithm. Fields in the structure are:
</p>
<ul>
<li> iterations
Number of iterations through loop.
</li><li> funcCount
Number of function evaluations.
</li><li> algorithm
The string <code>"bisection, interpolation"</code>.
</li><li> bracketx
A two-element vector with the final bracketing of the zero along the
x-axis.
</li><li> brackety
A two-element vector with the final bracketing of the zero along the
y-axis.
</li></ul>
<p><strong>See also:</strong> <a href="Linear-Least-Squares.html#XREFoptimset">optimset</a>, <a href="#XREFfsolve">fsolve</a>.
</p></dd></dl>
<hr>
<div class="header">
<p>
Next: <a href="Minimizers.html" accesskey="n" rel="next">Minimizers</a>, Up: <a href="Nonlinear-Equations.html" accesskey="u" rel="up">Nonlinear Equations</a> [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html" title="Index" rel="index">Index</a>]</p>
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