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Quotes:
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Basic Models    
(NEOS Solvers)
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where
x
is an
n dimensional vector and
f is a continuous real valued function.
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minimizex
cTx     subject to   
Ax = b,   x &ge 0
|
where
x
is an
n dimensional vector and
A is an
m by
n matrix.
(NEOS Sample Submissions)
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minimizex
xTQ x +cTx     subject to   
Ax = b,   Bx &ge d
|
where
x
is an
n dimensional vector and
A is an
m by
n matrix and
B is a
p by
n matrix.
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minimizeX
< C,X>     subject to   
Aop(X) = b,   X psd
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where
X
is a symmetric positive semidefinite matrix and
Aop is a linear transformation.
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minimizex f(x)
subject to   
gk(x) &le bk,   k = 1,...,m
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where
x
is an
n dimensional vector and
f and
gk are real valued
(sufficiently smooth) functions.
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(from Luenberger text, 1969)
   
A nonconvex problem with strong duality (TRS pg 5-14)
Supplementary Information
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the ubiquitous online source for
optimization.
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