Subgradient method: Difference between revisions

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{{Short description|Concept in convex optimization mathematics}}

'''Subgradient methods''' are [[iterative method]]s for solving [[convex optimization|convex minimization]] problems. Originally developed by [[Naum Z. Shor]] and others in the 1960s and 1970s, subgradient methods are convergent when applied even to a non-differentiable objective function. Subgradient methods are slower than Newton's method when applied to minimize twice continuously differentiable convex functions. However, Newton's method may fail to converge on problems that have non-differentiable kinks or polyhedral structure.

'''Subgradient methods''' are [[convex optimization]] methods which use [[Subderivative|subderivatives]]. Originally developed by [[Naum Z. Shor]] and others in the 1960s and 1970s, subgradient methods are convergent when applied even to a non-differentiable objective function. When the objective function is differentiable, sub-gradient methods for unconstrained problems use the same search direction as the method of [[gradient descent|gradient descent]].

Subgradient methods are slower than Newton's method when applied to minimize twice continuously differentiable convex functions. However, Newton's method fails to converge on problems that have non-differentiable kinks.

Depending on the algorithm, subgradient methods may use different strategies for determining the search direction and step length.

In recent years, some [[interior-point methods]] have been suggested for convex minimization problems, but subgradient projection methods and related bundle methods of descent remain competitive. For convex minimization problems with very large number of dimensions, subgradient-projection methods are suitable, because they require little storage.

* Using the steepest descent direction, but with step lengths that are computed according to a fixed strategy or from an estimate of the difference of the current function value from the optimum.

* Preprocessing the steepest descent direction by applying a [[linear transformation|transformation matrix]] , and then performing a line search or applying a step length selection strategy to the transformed search direction. ([[Naum Z. Shor|Naum Shor]]'s Space Dilation/R algorithms)

* Building a local model of the subdifferential of the function, and then applying [[trust region]] ideas. (Bundle Methods)

In recent years, some [[interior-point methods]] have been suggested for convex minimization problems, but subgradient projection methods and related bundle methods of descent remain competitive. For convex minimization problems with very large number of dimensions, subgradient-projection methods are suitable, because they require little storage.

Subgradient projection methods are often applied to large-scale problems with decomposition techniques. Such decomposition methods often allow a simple distributed method for a problem.

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==Classical subgradient rules==

Let <math>f : \mathbb{R}Reals^n \to \mathbb{R}Reals</math> be a [[convex function]] with ___domain <math>\mathbb{R}Reals^n.</math>.
A classical subgradient method iterates

:<math display=block>x^{(k+1)} = x^{(k)} - \alpha_k g^{(k)} \ </math>

where <math>g^{(k)}</math> denotes a''any'' [[subgradient]] of <math> f \ </math> at <math>x^{(k)}, \ </math>. and If<math>x^{(k)}</math> is the <math>k^{th}</math> iterate of <math>x.</math>
If <math>f \ </math> is differentiable, then its only subgradient is the gradient vector <math>\nabla f</math> itself.

It may happen that <math>-g^{(k)}</math> is not a descent direction for <math>f \ </math> at <math>x^{(k)}.</math>. We therefore maintain a list <math>f_{\rm{best}} \ </math> that keeps track of the lowest objective function value found so far, i.e.

:<math display=block>f_{\rm{best}}^{(k)} = \min\{f_{\rm{best}}^{(k-1)} , f(x^{(k)}) \}.</math>

===Step size rules===

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*Constant step size, <math>\alpha_k = \alpha.</math>

*Constant step length, <math>\alpha_k = \gamma/\lVert g^{(k)} \rVert_2,</math>, which gives <math>\lVert x^{(k+1)} - x^{(k)} \rVert_2 = \gamma.</math>

*Square summable but not summable step size, i.e. any step sizes satisfying <math display="block">\alpha_k\geq0,\qquad\sum_{k=1}^\infty \alpha_k^2 < \infty,\qquad \sum_{k=1}^\infty \alpha_k = \infty.</math>

:*Nonsummable diminishing, i.e. any step sizes satisfying <math display="block">\alpha_k \geq0geq 0,\qquad \sum_lim_{k=1}^\to\infty} \alpha_k^2 <= \infty0,\qquad \sum_{k=1}^\infty \alpha_k = \infty.</math>

*Nonsummable diminishing step lengths, i.e. <math>\alpha_k = \gamma_k/\lVert g^{(k)} \rVert_2,</math> where <math display="block">\gamma_k \geq 0,\qquad \lim_{k\to\infty} \gamma_k = 0,\qquad \sum_{k=1}^\infty \gamma_k = \infty.</math>

*Nonsummable diminishing, i.e. any step sizes satisfying

For all five rules, the step-sizes are determined "off-line", before the method is iterated; the step-sizes do not depend on preceding iterations. This "off-line" property of subgradient methods differs from the "on-line" step-size rules used for descent methods for differentiable functions: Many methods for minimizing differentiable functions satisfy Wolfe's sufficient conditions for convergence, where step-sizes typically depend on the current point and the current search-direction. An extensive discussion of stepsize rules for subgradient methods, including incremental versions, is given in the books by Bertsekas<ref>{{cite book

:<math>\alpha_k\geq0,\qquad \lim_{k\to\infty} \alpha_k = 0,\qquad \sum_{k=1}^\infty \alpha_k = \infty.</math>

| last = Bertsekas

*Nonsummable diminishing step lengths, i.e. <math>\alpha_k = \gamma_k/\lVert g^{(k)} \rVert_2</math>, where

| first = Dimitri P.

:<math>\gamma_k\geq0,\qquad \lim_{k\to\infty} \gamma_k = 0,\qquad \sum_{k=1}^\infty \gamma_k = \infty.</math>

| author-link = Dimitri P. Bertsekas

For all five rules, the step-sizes are determined "off-line", before the method is iterated; the step-sizes do not depend on preceding iterations. This "off-line" property of subgradient methods differs from the "on-line" step-size rules used for descent methods for differentiable functions: Many methods for minimizing differentiable functions satisfy Wolfe's sufficient conditions for convergence, where step-sizes typically depend on the current point and the current search-direction.

| title = Convex Optimization Algorithms

| edition = Second

| publisher = Athena Scientific

| year = 2015

| ___location = Belmont, MA.

| isbn = 978-1-886529-28-1

}}</ref> and by Bertsekas, Nedic, and Ozdaglar.<ref>{{cite book

|last1=Bertsekas

|first1=Dimitri P.

|last2=Nedic

|first2=Angelia

|last3 = Ozdaglar

|first3 = Asuman

| title = Convex Analysis and Optimization

| edition = Second

| publisher = Athena Scientific

| year = 2003

| ___location = Belmont, MA.

| isbn = 1-886529-45-0 }}</ref>

===Convergence results===

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| last = Bertsekas

| first = Dimitri P.

| authorlinkauthor-link = Dimitri P. Bertsekas

| title = Nonlinear Programming

| edition = Second

Line 50 ⟶ 68:

| last = Shor

| first = Naum Z.

| authorlinkauthor-link = Naum Z. Shor

| title = Minimization Methods for Non-differentiable Functions

| publisher = [[Springer-Verlag]]

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}}

</ref>

These classical subgradient methods have relatively poor performance and are no longer recommended for general use.<ref name="Lem"/><ref name="KLL"/> However, they are still used widely in specialized applications because they are simple and they can be easily adapted to take advantage of the special structure of the problem at hand.

==Subgradient-projection &and bundle methods==

During the 1970s, [[Claude Lemaréchal]] and Phil. Wolfe proposed "bundle methods" of descent for problems of convex minimization.<ref>

{{cite book

| last = Bertsekas

| first = Dimitri P.

| authorlinkauthor-link = Dimitri P. Bertsekas

| title = Nonlinear Programming

| edition = Second

Line 71 ⟶ 89:

| ___location = Cambridge, MA.

| isbn = 1-886529-00-0

}}

</ref> The meaning of the term "bundle methods" has changed significantly since that time. Modern versions and full convergence analysis were provided by Kiwiel.

<ref>

{{cite book|last=Kiwiel|first=Krzysztof|title=Methods of Descent for Nondifferentiable Optimization|publisher=[[Springer Verlag]]|___location=Berlin|year=1985|pages=362|isbn=978-3540156420 |mr=0797754}}

</ref> Contemporary bundle-methods often use "[[level set|level]] control" rules for choosing step-sizes, developing techniques from the "subgradient-projection" method of Boris T. Polyak (1969). However, there are problems on which bundle methods offer little advantage over subgradient-projection methods.<ref name="Lem">

{{cite book| last=Lemaréchal|first=Claude|authorlinkauthor-link=Claude Lemaréchal|chapter=Lagrangian relaxation|pages=112–156|title=Computational combinatorial optimization: Papers from the Spring School held in Schloß Dagstuhl, May 15–19, 2000|editor=Michael Jünger and Denis Naddef|series=Lecture Notes in Computer Science|volume=2241|publisher=Springer-Verlag| ___location=Berlin|year=2001|isbn=3-540-42877-1|mr=1900016|doi=10.1007/3-540-45586-8_4|refs2cid=harv9048698 }}</ref><ref name="KLL">

{{cite journal|last1=Kiwiel|first1=Krzysztof&nbsp;C.|last2=Larsson |first2=Torbjörn|last3=Lindberg|first3=P.&nbsp;O.|title=Lagrangian relaxation via ballstep subgradient methods|url=http://morrcin.journalorg.informs.orgpl/cgiContent/content139438/abstractPDF/32/3/669RB-2002-76.pdf |journal=[[Mathematics of Operations Research]]|volume=32|yeardate=August 2007|number=3|pages=669–686|month=August|mr=2348241|doi=10.1287/moor.1070.0261|ref=harv}}

</ref>

==Constrained optimization==

===Projected subgradient===

One extension of the subgradient method is the '''projected subgradient method''', which solves the constrained [[Mathematical optimization|optimization]] problem

:minimize <math>f(x) \ </math> subject to <math display=block>x \in \mathcal{C}</math>

:<math>x\in\mathcal{C}</math>

where <math>\mathcal{C}</math> is a convex set. The projected subgradient method uses the iteration

:<math>x^{(k+1)} = P \left(x^{(k)} - \alpha_k g^{(k)} \right) </math>

where <math>\mathcal{C}</math> is a [[convex set]].

The projected subgradient method uses the iteration

<math display=block>x^{(k+1)} = P \left(x^{(k)} - \alpha_k g^{(k)}\right)</math>

where <math>P</math> is projection on <math>\mathcal{C}</math> and <math>g^{(k)}</math> is any subgradient of <math>f \ </math> at <math>x^{(k)}.</math>

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The subgradient method can be extended to solve the inequality constrained problem

:minimize <math>f_0(x) \ </math> subject to <math display=block>f_i (x) \leq 0,\quad i = 1,\ldots,m</math>

:<math>f_i (x) \leq 0,\quad i = 1,\dots,m</math>

where <math>f_i</math> are convex. The algorithm takes the same form as the unconstrained case

<math display=block>x^{(k+1)} = x^{(k)} - \alpha_k g^{(k)} \ </math>

:<math>x^{(k+1)} = x^{(k)} - \alpha_k g^{(k)} \ </math>

where <math>\alpha_k>0</math> is a step size, and <math>g^{(k)}</math> is a subgradient of the objective or one of the constraint functions at <math>x. \ </math> Take

<math display=block>g^{(k)} =

:<math>g^{(k)} =

\begin{cases}

\partial f_0 (x) & \text{ if } f_i(x) \leq 0 \; \forall i = 1 \dots m \\

\partial f_j (x) & \text{ for some } j \text{ such that } f_j(x) > 0

\end{cases}</math>

where <math>\partial f</math> denotes the [[subdifferential]] of <math>f. \ </math> If the current point is feasible, the algorithm uses an objective subgradient; if the current point is infeasible, the algorithm chooses a subgradient of any violated constraint.

==See also==

where <math>\partial f</math> denotes the subdifferential of <math>f \ </math>. If the current point is feasible, the algorithm uses an objective subgradient; if the current point is infeasible, the algorithm chooses a subgradient of any violated constraint.

* {{annotated link|Stochastic gradient descent}}

==References==

<references/>

{{reflist}}

==Further reading==

* {{cite book

| last = Bertsekas

| first = Dimitri P.

| authorlink = Dimitri P. Bertsekas

| title = Nonlinear Programming

| publisher = Athena Scientific

| year = 1999

| ___location = CambridgeBelmont, MA.

| isbn = 1-886529-00-0

}}

* {{cite book

|last1=Bertsekas

|first1=Dimitri P.

|last2=Nedic

|first2=Angelia

|last3 = Ozdaglar

|first3 = Asuman

| title = Convex Analysis and Optimization

| edition = Second

| publisher = Athena Scientific

| year = 2003

| ___location = Belmont, MA.

| isbn = 1-886529-45-0

}}

* {{cite book

| last = Bertsekas

| first = Dimitri P.

| title = Convex Optimization Algorithms

| publisher = Athena Scientific

| year = 2015

| ___location = Belmont, MA.

| isbn = 978-1-886529-28-1

}}

* {{cite book

| last = Shor

| first = Naum Z.

| authorlink = Naum Z. Shor

| title = Minimization Methods for Non-differentiable Functions

| publisher = [[Springer-Verlag]]

Line 133 ⟶ 174:

| year = 1985

}}

* {{cite book|last=Ruszczyński|author-link=Andrzej Piotr Ruszczyński|first=Andrzej|title=Nonlinear Optimization|publisher=[[Princeton University Press]]|___location=Princeton, NJ|year=2006|pages=xii+454|isbn=978-0691119151 |mr=2199043}}

==External links==

* [http://www.stanford.edu/class/ee364a/ EE364A] and [http://www.stanford.edu/class/ee364b/ EE364B], Stanford's convex optimization course sequence.

{{Convex analysis and variational analysis}}

{{optimization algorithms|convex}}

[[Category:MathematicalConvex optimizationanalysis]]

[[Category:Convex optimization]]

[[Category:Mathematical optimization]]

[[Category:Optimization algorithms and methods]]

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