We propose a new gradient method for quadratic programming, named SDC, which alternates some steepest descent (SD) iterates with some gradient iterates that use a constant steplength computed through the Yuan formula. The SDC method exploits the asymptotic spectral behaviour of the Yuan...

When solving nonlinear least-squares problems, it is often useful to regularize the problem using a quadratic term, a practice which is especially common in applications arising in inverse calculations. A solution method derived from a trust-region Gauss-Newton algorithm is analyzed for such...

In recent years, convex optimization methods were successfully applied for various image processing tasks and a large number of first-order methods were designed to minimize the corresponding functionals. Interestingly, it was shown recently in Grewenig et al. (<CitationRef CitationID="CR24">2010</CitationRef>) that the simple idea of...</citationref>

The conjugate gradient method is widely used in unconstrained optimization, especially for large-scale problems. Recently, Zhang et al. proposed a three-term PRP method (TTPRP) and a three-term HS method (TTHS), both of which can produce sufficient descent conditions. In this paper, the global...

Drei große Reformenpakete und eine Reihe kleinerer Begleitmaßnahmen prägen das renten¬politische Erbe der rot-grünen Bundesregierung. Einerseits greifen sie Trends in der Reformpolitik seit Beginn der 90er Jahre auf. So verstärkt die Rentenstrukturreform 2001 beispielsweise die...

We are concerned with the solution of the bound constrained minimization problem {minf(x), l≤x≤u}. For the solution of this problem we propose an active set method that combines ideas from projected and nonmonotone Newton-type methods. It is based on an iteration of the form x <Superscript> k+1</Superscript>=[x <Superscript> k </Superscript>+α...</superscript></superscript>