Since direct numerical solution of a non-convex variational problem (P) yields rapid oscillations, we study the relaxed problem (RP) which is a degenerate convex minimization problem. The classical example for such a relaxed variational problem is the double-well problem. In an earlier work, the authors howed that relaxation is not linked to a loss of information if our main interest concerns the macroscopic displacement field, the stress field or the microstructure. Furthermore, a priori and a posteriori error estimates have been computed and an adaptive algorithm was proposed for this class of degenerate variational problems. This paper addresses the question of efficiency and considers the ZZ-indicator, named after Zienkiewicz and Zhu, a...
We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms...
Infimalfolgen nichtkonvexer Variationsprobleme haben aufgrund feiner Oszillationen häufig keinen sta...
This paper deals with new variable-metric algorithms for nonsmooth optimization problems, the so-cal...
Abstract. This paper addresses the numerical approximation of Young measures appear-ing as generaliz...
We investigate the numerical approximation of Young measure solutions appearing as generalised solut...
Abstract. The computational nonlinear PDEs involve minimisation problems with various striking chall...
Degenerate variational problems often result from a relaxation technique in effective numerical simu...
Abstract: "An implementation of the stochastic gradient minimization method is proposed as a viable ...
Motivated by variational models in continuum mechanics, we introduce a novel algorithm for performin...
summary:Multidimensional vectorial non-quasiconvex variational problems are relaxed by means of a ge...
Abstract. Degenerate variational problems often result from a relaxation technique in effective nume...
Abstract We introduce a class of adaptive non-smooth convex variational problems for image denoising...
The minimization of a functional composed of a nonsmooth and nonadditive regularization term and a c...
AbstractNon-convex variational problems in many situations lack a classical solution. Still they can...
summary:The scalar nonconvex variational problems of the minimum-energy type on Sobolev spaces are s...
We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms...
Infimalfolgen nichtkonvexer Variationsprobleme haben aufgrund feiner Oszillationen häufig keinen sta...
This paper deals with new variable-metric algorithms for nonsmooth optimization problems, the so-cal...
Abstract. This paper addresses the numerical approximation of Young measures appear-ing as generaliz...
We investigate the numerical approximation of Young measure solutions appearing as generalised solut...
Abstract. The computational nonlinear PDEs involve minimisation problems with various striking chall...
Degenerate variational problems often result from a relaxation technique in effective numerical simu...
Abstract: "An implementation of the stochastic gradient minimization method is proposed as a viable ...
Motivated by variational models in continuum mechanics, we introduce a novel algorithm for performin...
summary:Multidimensional vectorial non-quasiconvex variational problems are relaxed by means of a ge...
Abstract. Degenerate variational problems often result from a relaxation technique in effective nume...
Abstract We introduce a class of adaptive non-smooth convex variational problems for image denoising...
The minimization of a functional composed of a nonsmooth and nonadditive regularization term and a c...
AbstractNon-convex variational problems in many situations lack a classical solution. Still they can...
summary:The scalar nonconvex variational problems of the minimum-energy type on Sobolev spaces are s...
We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms...
Infimalfolgen nichtkonvexer Variationsprobleme haben aufgrund feiner Oszillationen häufig keinen sta...
This paper deals with new variable-metric algorithms for nonsmooth optimization problems, the so-cal...