Chapter 4. Subdifferential calculus for convex functions
Chapter 4.
Subdifferential calculus for convex functions
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Chapter 4. Subdifferential calculus for convex functions
Directional derivative
Definition. Let f : IR
n
→ IR ∪ {+∞}, x ∈ domf and d ∈ IR
n
. The
directional derivative of f at x in the direction d is
f

(x, d) = lim
t↓0
f (x + td) − f (x)
t
if the limit exists (-∞ and ∞ are allowed)
Proposition. Let f : IR
n
→ IR ∪ {+∞} be a convex function, and let
x ∈ dom f . For each d, the difference quotient in the definition of
f

(x, d) is a non-decreasing function of t > 0, so that
f

(x, d) = inf
t>0
f (x + t d) − f (x)

∗
, y − x
This leads to the following definition.
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Chapter 4. Subdifferential calculus for convex functions
Subgradient. Subdifferential
Definition. Let f : IR
n
→ IR ∪ {+∞} be a convex function. A vector
x
∗
is said to be a subgradient of f at x if
∀y ∈ IR
n
f (y) ≥ f (x) + x
∗
, y − x.
The set of subgradients of f at x is called the subdifferential of f at x
and is denoted by ∂f (x).
Proposition. The subdifferential ∂f (x) is a closed convex set. It may
be empty.
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Chapter 4. Subdifferential calculus for convex functions
Examples
f (x) = |x|
∂f (0) = [−1, 1], ∂f (x) = {1} if x > 0, ∂f (x) = −1 if x < 0
f (x) = e
x
− 1 if x ≥ 0 and 0 if x < 0
∂f (0) = [0, 1], ∂f (x) = {e

that case f

(x, y) is closed and proper as a function of y , and
f

(x, d) = sup{< x
∗
, d > |x
∗
∈ ∂f (x)} = δ
∗
(d|∂f (x)).
Finally, ∂f (x) is a non-empty bounded set if and only if x ∈ int(domf ),
in which case f

(x, d) is finite for every d.
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