- differentiable. The set of
subderivatives at a
point is
called the
subdifferential at that point.
Subderivatives arise in
convex analysis, the
study of...
-
respect to z {\displaystyle z} for all x {\displaystyle x} , then the
subdifferential of f ( x ) {\displaystyle f(x)} is
given by ∂ f ( x ) = c o n v { ∂...
- (x)}{\theta }},} is
decreasing as θ
approaches 0+. In particular, the
subdifferential of φ {\displaystyle \varphi }
evaluated at x in the
direction y is...
- point,
including at the origin.
Everywhere except zero, the
resulting subdifferential consists of a
single value,
equal to the
value of the sign function...
- f:X\to [-\infty ,\infty ]} and x ∈ X {\displaystyle x\in X} then the
subdifferential set is ∂ f ( x ) : = { x ∗ ∈ X ∗ : f ( z ) ≥ f ( x ) + ⟨ x ∗ ,...
- optimization. In particular, he made
contributions to the
calculus of
subdifferentials for vector-lattice
valued functions, to
whose study he
introduced methods...
- f} .
Since the
subdifferential of a proper, convex,
lower semicontinuous function on a
Hilbert space is
inverse to the
subdifferential of its
convex conjugate...
-
solution to be optimal. If some of the
functions are non-differentiable,
subdifferential versions of Karush–Kuhn–Tucker (KKT)
conditions are available. Under...
- }}f_{j}(x)>0\end{cases}}}
where ∂ f {\displaystyle \partial f}
denotes the
subdifferential of f . {\displaystyle f.\ } If the
current point is feasible, the...
- f {\displaystyle f} at x {\displaystyle x} (also
called the
Clarke subdifferential) is
given as ∂ ∘ f ( x ) := { ξ ∈ R n : ⟨ ξ , v ⟩ ≤ f ∘ ( x , v ) ...
