We compare the performance of several robust large-scale minimization algorithms for the unconstrained minimization of an ill-posed inverse problem. The parabolized Navier-Stokes equations model was used for adjoint parameter estimation. The methods compared consist of two versions of the nonlinear conjugate gradient method (CG), Quasi-Newton (BFGS), the limited memory Quasi-Newton (L-BFGS) [15...

We present a generalized Newton method and a quasiNewton method for solving H(x) := F(nc(x))+x-nc(x) = 0, when C is a polyhedral set. For both the Newton and quasi-Newton methods considered here, the subproblem to be solved is a linear system of equations per iteration. The other characteristics of the quasi-Newton method include: (i) a g-superlinear convergence theorem is established without a...

We investigate aspects of effectivity and computability on open, closed and quasi-compact sets as well as partial continuous functions in computable T0spaces. A computable T0-space is a second countable T0-spaces with a notation of a base whose domain is recursive and computable intersection on the base. As we do not suppose our space to be a Hausdorff space, we don’t talk about compact subsets...

Quasi-Newton and truncated-Newton methods are popular methods in optimization, and are traditionally seen as useful alternatives to the gradient and Newton methods. Throughout the literature, results are found that link quasi-Newton methods to certain first-order methods under various assumptions. We offer a simple proof to show that a range of quasi-Newton methods are first-order methods in th...

In this paper we follow up our discussion on algorithms suitable for optimization of systems governed by partial differential equations. In the first part of of this paper we proposed a Lagrange-Newton-Krylov-Schur method (LNKS) that uses Krylov iterations to solve the Karush-Kuhn-Tucker system of optimality conditions, but invokes a preconditioner inspired by reduced space quasi-Newton algorit...