# Convex Iteration

### From Wikimization

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Convex iteration is method for constraining rank or cardinality in an otherwise convex optimization problem. A rank or cardinality constraint is replaced by a weighted linear regularization term added to the objective, and then two convex problems are iterated until convergence where, ideally, solution to the original problem is found. | Convex iteration is method for constraining rank or cardinality in an otherwise convex optimization problem. A rank or cardinality constraint is replaced by a weighted linear regularization term added to the objective, and then two convex problems are iterated until convergence where, ideally, solution to the original problem is found. | ||

+ | |||

+ | ==constraining cardinality of signed variable== | ||

+ | Now consider a feasibility problem equivalent to the classical problem from linear algebra | ||

+ | <math>A_{}x_{\!}=_{\!}b</math>, | ||

+ | but with an upper bound <math>k</math> on cardinality <math>\|x\|_0</math> : | ||

+ | for vector <math>b\!\in\!\mathcal{R}(A)</math> | ||

+ | |||

+ | <math>\begin{array}{rl}\text{find}&_{}x\in_{}\reals^n\\ | ||

+ | \mbox{subject to}&A_{}x=b\\ | ||

+ | &\|x\|_0\leq_{}k | ||

+ | \end{array}~~~~~~~~~~(1)</math> | ||

+ | |||

+ | where <math>\|x\|_{0\!}\leq_{_{}\!}k</math> means vector <math>x</math> has at most <math>k</math> nonzero entries; | ||

+ | such a vector is presumed existent in the feasible set. <br>Convex iteration works with a nonnegative variable; | ||

+ | absolute value <math>|x|</math> is therefore needed. | ||

+ | We propose that problem (1) can be equivalently written | ||

+ | |||

+ | <math>\begin{array}{cl}\text{minimize}_{x_{}\in_{_{}}\mathbb{R}^{^n},~y_{}\in_{_{}}\mathbb{R}^{^n}}&\langle_{}|x|_{}\,,\,y^{}\rangle\\ | ||

+ | \mbox{subject to}&A_{}x=b\\ | ||

+ | &0\preceq y\preceq\mathbf{1}\\ | ||

+ | &y^T\mathbf{1}=n-_{}k | ||

+ | \end{array}</math> | ||

+ | |||

+ | which moves the cardinality constraint to the objective. | ||

+ | To express this nonconvex problem as a convex iteration, we separate it into halves: | ||

+ | for <math>\varepsilon</math> a relatively small positive constant, | ||

+ | |||

+ | <math>\begin{array}{rl}\text{minimize}_{x_{}\in_{_{}}\mathbb{R}^{^n},~t_{}\in_{_{}}\mathbb{R}^{^n}}&\langle t_{}\,,\,y+\varepsilon^{}\mathbf{1}\rangle\\ | ||

+ | \mbox{subject to}&A_{}x=b\\ | ||

+ | &-t\preceq x\preceq_{_{}}t | ||

+ | \end{array}~~~~~~~~~~~~~~(2)</math> | ||

+ | |||

+ | by iterating with | ||

+ | |||

+ | <math>\begin{array}{cl}\text{minimize}_{y_{}\in_{_{}}\mathbb{R}^{^n}}&\langle t^\star,\,y+\varepsilon^{}\mathbf{1}\rangle\\ | ||

+ | \mbox{subject to}&0\preceq y\preceq\mathbf{1}\\ | ||

+ | &y^T\mathbf{1}=n-_{}k | ||

+ | \end{array}~~~~~~~~~~~~~~~~~~~(3)</math> | ||

+ | |||

+ | to find direction vector <math>y</math>. | ||

+ | The term <math>\langle t\,,_{_{}}\varepsilon^{}\mathbf{1}\rangle</math> in (2) is necessary to determine absolute value | ||

+ | <math>|x|_{\!}=_{}t^{\star_{}}</math> because vector <math>y</math> can take zero values in its entries; | ||

+ | a good estimate of <math>|x|</math> is required for (3). | ||

+ | |||

+ | By initializing direction vector <math>y</math> to <math>(1\!-_{}\!\varepsilon)\mathbf{1}_{}</math>, | ||

+ | the first iteration of problem (2) is a 1-norm problem; ''i.e.'', | ||

+ | |||

+ | <math>\begin{array}{ccc} | ||

+ | \begin{array}{rl}\text{minimize}_{x_{}\in_{_{}}\mathbb{R}^{^n},~t_{}\in_{_{}}\mathbb{R}^{^n}}&\langle t\,,_{}\mathbf{1}\rangle\\ | ||

+ | \mbox{subject to}&A_{}x=b\\ | ||

+ | &-t\preceq x\preceq_{_{}}t | ||

+ | \end{array} | ||

+ | &\equiv&~ | ||

+ | \begin{array}{cl}\text{minimize}_{x_{}\in_{_{}}\mathbb{R}^{^n}}&\|x\|_1\\ | ||

+ | \mbox{subject to}&A_{}x=b | ||

+ | \end{array} | ||

+ | \end{array}~~~~~~~~~~(4)</math> | ||

+ | |||

+ | Subsequent iterations of problem (2) engaging cardinality term <math>\langle t_{}\,,\,y\rangle</math> | ||

+ | can be interpreted as corrections to this 1-norm problem leading to a 0-norm solution. | ||

+ | |||

+ | Iteration (2) (3) always converges to a locally optimal solution by virtue of | ||

+ | a monotonically nonincreasing objective sequence. | ||

+ | There can be no proof of global optimality. |

## Revision as of 20:07, 5 February 2008

Convex iteration is method for constraining rank or cardinality in an otherwise convex optimization problem. A rank or cardinality constraint is replaced by a weighted linear regularization term added to the objective, and then two convex problems are iterated until convergence where, ideally, solution to the original problem is found.

## constraining cardinality of signed variable

Now consider a feasibility problem equivalent to the classical problem from linear algebra , but with an upper bound on cardinality : for vector

where means vector has at most nonzero entries;
such a vector is presumed existent in the feasible set.

Convex iteration works with a nonnegative variable;
absolute value is therefore needed.
We propose that problem (1) can be equivalently written

which moves the cardinality constraint to the objective. To express this nonconvex problem as a convex iteration, we separate it into halves: for a relatively small positive constant,

by iterating with

to find direction vector . The term in (2) is necessary to determine absolute value because vector can take zero values in its entries; a good estimate of is required for (3).

By initializing direction vector to ,
the first iteration of problem (2) is a 1-norm problem; *i.e.*,

Subsequent iterations of problem (2) engaging cardinality term can be interpreted as corrections to this 1-norm problem leading to a 0-norm solution.

Iteration (2) (3) always converges to a locally optimal solution by virtue of a monotonically nonincreasing objective sequence. There can be no proof of global optimality.