Iterated linear optimization

Iterated Function Systems Optimization

Bounding Iterated Function Systems via Convex Optimization

We present an algorithm to construct a bounding polyhedron for an affine Iterated Function System (IFS). Our algorithm expresses the IFS-bounding problem as a set of linear constraints on a linear objective function, which can then be solved via standard techniques for linear convex optimization. As such, our algorithm is guaranteed to find the optimum recursively instantiable bounding volume, ...

On Iterated Minimization in Nonconvex Optimization

In dynamic programming and decomposition methods one often applies an iterated minimization procedure. The problem variables are partitioned into several blocks, say x and y. Treating y as a parameter, the first phase consists of minimization with respect to the variable x. In a second phase the minimization of the resulting optimal value function depending on y is considered. In this paper we ...

Inexact Newton-type Optimization with Iterated

This paper presents and analyzes an Inexact Newton-type optimization method 4 based on Iterated Sensitivities (INIS). A particular class of Nonlinear Programming (NLP) problems 5 is considered, where a subset of the variables is defined by nonlinear equality constraints. The pro6 posed algorithm considers any problem-specific approximation for the Jacobian of these constraints. 7 Unlike other i...

Iterated Function Systems Optimization with Genetic Algorithms

This paper presents a genetic algorithm used to infer Iterated Function Systems (IFSs) for approximation of 1D and 2D data sets. First, we give an introduction to IFSs. Secondly, we discuss the encoding of 1D and 2D IFSs in the form of chromosomes, and present genetic operators to work upon these representations. The performance of the genetic algorithm is evaluated on four 1D and two 2D test s...