3.3. Operations on concave functions and duality
In this section we consider the basic operations on concave functions
and their interplay with the Legendre-Fenchel duality.
Let and be two concave functions such that their
stability sets are not disjoint.
Their sup-convolution
is the function
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This is a concave function whose effective domain is the Minkowski
sum .
This operation is associative and
commutative whenever the terms are defined.
The operations of pointwise addition and sup-convolution
are dual to each other.
When working with general concave functions, there are some technical issues
in this duality that will disappear when considering uniform limits of
piecewise affine concave functions.
Proposition 3.38.
Let be concave
functions.
- (1)
If ,
then
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- (2)
If , then
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- (3)
If , then
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Proof.
This is proved in [Roc70, Theorem 16.4].
∎
Remark 3.39.
When some of the , say
, are piecewise affine, the statement
(3) of the previous proposition
holds under the weaker hypothesis [Roc70, Theorem 20.1]
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Let be a concave function. For , the left
and right scalar multiplication of by
are the functions defined, for
,
by
and respectively.
For a point ,
the translate of by
is the concave
function defined as
for .
Proposition 3.40.
Let be a concave function on , , and . Then
- (1)
,
and ;
- (2)
, and ;
- (3)
,
and
;
- (4)
, and
.
Proof.
This follows easily from the
definitions.
∎
We next consider direct and inverse images of concave functions
by affine maps.
Let be a another finite dimensional real vector space and
set for its dual space. For a linear map
we denote by
the dual map.
We need the following lemma in order to properly define direct images.
Lemma 3.41.
Let be a linear map and a concave
function on . If then, for all ,
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Proof.
Let such that
. By the definition of the stability set, .
Thus, for any ,
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and so is bounded above, as stated.
∎
Definition 3.42.
Let be an affine map
defined as for a linear map
and a point .
Let be a concave function on
such that and a concave function on
such that . Then
the inverse image of by
is defined as
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and the direct image of by
is defined as
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It is easy to see that the inverse image is concave
with effective domain .
Similarly, the direct image
is concave with effective domain
, thanks to
Lemma 3.41.
The inverse image of a closed function is also closed.
In contrast, the direct image
of a closed function is not necessarily closed:
consider for instance the indicator function of the set , which is a closed concave function. Let be the first projection. Then is the
indicator function of the subset , which is not a closed
concave function.
We now turn to the behaviour of the sup-differential with respect to the basic operations.
A first important property is the additivity.
Proposition 3.43.
For each , let be a concave function and
a real number.
Then
- (1)
;
- (2)
if , then
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Proof.
This is [Roc70, Theorem 23.8].
∎
As in Remark 3.39, if are
piecewise affine, then (3.44) holds under the weaker hypothesis
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The following result gives the behaviour of the sup-differential with respect to linear
maps
Proposition 3.45.
Let be a linear map, and
the associated affine map. Let be a concave function on
, then
- (1)
for all
;
- (2)
if either or is
piecewise affine and
, then for all we have
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Proof.
The linear case is [Roc70, Theorem 23.9]. The general
case follows from the linear case and the commutativity of
the sup-differential and the translation.
∎
We summarize the behaviour of direct and inverse images of affine maps
with respect to the Legendre-Fenchel duality.
Proposition 3.46.
Let be an affine map defined as for a linear map
and a point . Let be a concave function on
such that and a concave function on
such that . Then
- (1)
and
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- (2)
and
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- (3)
if then
and, for all in this set,
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Moreover, for ,
a point realizes this maximum if and only if
for a such that .
Observe that the last assertion in the above proposition can be also
expressed as
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Proof.
By Proposition 3.40(3,4),
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Then, except for the last assertion, the result follows by combining
this with the case when is a linear map, treated in [Roc70, Theorem
16.3].
To prove the last assertion of the proposition, we first note that
the concave function
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attains its maximum at a point if and only if its sup-differential at contains
.
We fix a point in and
we consider the affine inclusion
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We denote by the dual
of the linear part of .
Set , then for
, by Proposition 3.45, we have
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and so if and only if .
Hence
realizes the maximum if and only if
for some such that , as stated.
∎
In particular, the operations of direct and inverse image of
linear maps are dual to each other.
In the notation of Proposition 3.46 and assuming for simplicity
, we have
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while the stability sets relate by
and .
The last concept we recall in this section is the notion of recession of
a concave function.
Definition 3.48.
The recession function of a
concave function ,
denoted , is the function
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This is a concave conical function. If is
closed, its recession function can be defined as the limit
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for any [Roc70, Theorem 8.5].
It is clear from the definition that . The equality does not hold in general, as can be seen
by considering the concave function , .
If
is closed then the function is closed [Roc70, Theorem
8.5].
Hence it is natural to regard recession functions as support functions.
Proposition 3.50.
Let be a concave function. Then is the support function of
.
If is closed, then
is the support function of .
Proof.
This is [Roc70, Theorem 13.3].
∎