<s>
In	O
constrained	B-Algorithm
least	I-Algorithm
squares	I-Algorithm
one	O
solves	O
a	O
linear	B-Algorithm
least	I-Algorithm
squares	I-Algorithm
problem	O
with	O
an	O
additional	O
constraint	O
on	O
the	O
solution	O
.	O
</s>
<s>
Equality	B-Algorithm
constrained	I-Algorithm
least	O
squares	O
:	O
the	O
elements	O
of	O
must	O
exactly	O
satisfy	O
(	O
see	O
Ordinary	O
least	O
squares	O
)	O
.	O
</s>
<s>
Stochastic	O
(	O
linearly	O
)	O
constrained	B-Algorithm
least	I-Algorithm
squares	I-Algorithm
:	O
the	O
elements	O
of	O
must	O
satisfy	O
,	O
where	O
is	O
a	O
vector	O
of	O
random	O
variables	O
such	O
that	O
and	O
.	O
</s>
<s>
This	O
effectively	O
imposes	O
a	O
prior	O
distribution	O
for	O
and	O
is	O
therefore	O
equivalent	O
to	O
Bayesian	B-General_Concept
linear	I-General_Concept
regression	I-General_Concept
.	O
</s>
<s>
Non-negative	B-Algorithm
least	I-Algorithm
squares	I-Algorithm
(	O
NNLS	O
)	O
:	O
The	O
vector	O
must	O
satisfy	O
the	O
vector	O
inequality	O
defined	O
componentwise	O
—	O
that	O
is	O
,	O
each	O
component	O
must	O
be	O
either	O
positive	O
or	O
zero	O
.	O
</s>
<s>
where	O
is	O
a	O
projection	B-Algorithm
matrix	I-Algorithm
.	O
</s>
