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Subgradient methods are algorithms for solving convex optimization problems. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient methods can be used with a non-differentiable objective function. When the objective function is differentiable, subgradient methods for unconstrained problems use the same search direction as the method of steepest descent.## Basic subgradient update

### Step size rules

### Convergence results

## Constrained optimization

### Projected subgradient

One extension of the subgradient method is the projected subgradient method, which solves the constrained optimization problem
### General constraints

## References

*## External links

Although subgradient methods can be much slower than interior-point methods and Newton's method in practice, they can be immediately applied to a far wider variety of problems and require much less memory. Moreover, by combining the subgradient method with primal or dual decomposition techniques, it is sometimes possible to develop a simple distributed algorithm for a problem.

Let $f:mathbb\{R\}^n\; to\; mathbb\{R\}$ be a convex function with domain $mathbb\{R\}^n$. The subgradient method uses the iteration

- $x^\{(k+1)\}\; =\; x^\{(k)\}\; -\; alpha\_k\; g^\{(k)\}$

- $f\_\{rm\{best\}\}^\{(k)\}\; =\; min\{f\_\{rm\{best\}\}^\{(k-1)\}\; ,\; f(x^\{(k)\})\; \}.$

Many different types of step size rules are used in the subgradient method. Five basic step size rules for which convergence is guaranteed are:

- Constant step size, $alpha\_k\; =\; alpha.$
- Constant step length, $alpha\_k\; =\; gamma/lVert\; g^\{(k)\}\; rVert\_2$, which gives $lVert\; x^\{(k+1)\}\; -\; x^\{(k)\}\; rVert\_2\; =\; gamma.$
- Square summable but not summable step size, i.e. any step sizes satisfying

- $alpha\_kgeq0,qquadsum\_\{k=1\}^infty\; alpha\_k^2\; <\; infty,qquad\; sum\_\{k=1\}^infty\; alpha\_k\; =\; infty.$

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

- $alpha\_kgeq0,qquad\; lim\_\{ktoinfty\}\; alpha\_k\; =\; 0,qquad\; sum\_\{k=1\}^infty\; alpha\_k\; =\; infty.$

- Nonsummable diminishing step lengths, i.e. $alpha\_k\; =\; gamma\_k/lVert\; g^\{(k)\}\; rVert\_2$, where

- $gamma\_kgeq0,qquad\; lim\_\{ktoinfty\}\; gamma\_k\; =\; 0,qquad\; sum\_\{k=1\}^infty\; gamma\_k\; =\; infty.$

For constant step size and constant step length, the subgradient algorithm is guaranteed to converge to within some range of the optimal value, i.e.,

- $lim\_\{ktoinfty\}\; f\_\{rm\{best\}\}^\{(k)\}\; -\; f^*math>$

- minimize $f(x)$ subject to

- $xinmathcal\{C\}$

where $mathcal\{C\}$ is a convex set. The projected subgradient method uses the iteration

- $x^\{(k+1)\}\; =\; P\; left(x^\{(k)\}\; -\; alpha\_k\; g^\{(k)\}\; right)$

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

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

- minimize $f\_0(x)$ subject to

- $f\_i\; (x)\; leq\; 0,quad\; i\; =\; 1,dots,m$

where $f\_i$ are convex. The algorithm takes the same form as the unconstrained case

- $x^\{(k+1)\}\; =\; x^\{(k)\}\; -\; alpha\_k\; g^\{(k)\}$

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

- $g^\{(k)\}\; =$

partial f_0 (x) & f_i(x) leq 0,quad i = 1,dots,m

partial f_j (x) & f_j(x) > 0end{cases}

where $partial\; f$ denotes the subdifferential of $f$. 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.

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Last updated on Wednesday August 20, 2008 at 17:29:59 PDT (GMT -0700)

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This article is licensed under the GNU Free Documentation License.

Last updated on Wednesday August 20, 2008 at 17:29:59 PDT (GMT -0700)

View this article at Wikipedia.org - Edit this article at Wikipedia.org - Donate to the Wikimedia Foundation

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