- v\cdot (x-x_{0}),}
where the dot
denotes the dot product. The set of all
subgradients at x 0 {\displaystyle x_{0}} is
called the
subdifferential at x 0 {\displaystyle...
- Ozdaglar. For
constant step-length and
scaled subgradients having Euclidean norm
equal to one, the
subgradient method converges to an
arbitrarily close approximation...
- size rules,
which were
first developed for
classical subgradient methods.
classical subgradient methods using divergent-series
rules are much
slower than...
-
secant equation.
Although the
method involves subgradients, it is
distinct from his so-called
subgradient method described above. N. Z. Shor and N. G....
-
measure theory and
probability theory,
number theory, and
approximate subgradients and coderivatives. He
latterly collaborated with his son,
Jonathan Borwein...
- that
evaluate gradients, or
approximate gradients in some way (or even
subgradients):
Coordinate descent methods:
Algorithms which update a
single coordinate...
- Cutting-plane
methods Ellipsoid method Subgradient method Dual
subgradients and the drift-plus-penalty
method Subgradient methods can be
implemented simply...
- z_{t}\rangle } . To
generalise the
algorithm to any
convex loss function, the
subgradient ∂ v t ( w t ) {\displaystyle \partial v_{t}(w_{t})} of v t {\displaystyle...
-
include coordinate descent,
subgradient methods, least-angle
regression (LARS), and
proximal gradient methods.
Subgradient methods are the
natural generalization...
- 604861. Kiwiel,
Krzysztof C. (2001). "Convergence and
efficiency of
subgradient methods for
quasiconvex minimization".
Mathematical Programming, Series...
