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Next:&nbsp;<a rel="next" accesskey="n" href="gjp.html#gjp">gjp</a>,
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<h3 class="section">5.1 A complementary pivoting algorithm</h3>

<p><a name="index-cpiv_005fbard-88"></a>

<h4 class="subheading">Helptext:</h4>

<p><a name="XREFcpiv_005fbard"></a>
<pre class="verbatim">[lb, idx, ridx, mv] = cpiv_bard (v, m[, incl])

v: column vector; m: matrix; incl (optional): index. length (v)
must equal rows (m). Finds column vectors w and l with w == v + m *
l, w >= 0, l >= 0, l.' * w == 0. Chooses idx, w, and l so that
l(~idx) == 0, l(idx) == -inv (m(idx, idx)) * v(idx), w(idx) roughly
== 0, and w(~idx) == v(~idx) + m(idx, ~idx).' * l(idx). idx indexes
at least everything indexed by incl, but l(incl) may be &lt; 0. lb:
l(idx) (column vector); idx: logical index, defined above; ridx:
~idx &amp; w roughly == 0; mv: [m, v] after performing a Gauss-Jordan
'sweep' (with gjp.m) on each diagonal element indexed by idx.
Except the handling of incl (which enables handling of equality
constraints in the calling code), this is called solving the
'complementary pivot problem' (Cottle, R. W. and Dantzig, G. B.,
'Complementary pivot theory of mathematical programming', Linear
Algebra and Appl. 1, 102--125. References for the current
algorithm: Bard, Y.: Nonlinear Parameter Estimation, p. 147--149,
Academic Press, New York and London 1974; Bard, Y., 'An eclectic
approach to nonlinear programming', Proc. ANU Sem. Optimization,
Canberra, Austral. Nat. Univ.).

</pre>

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