A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Ant-based load balancing in telecommunications networks
Adaptive Behavior
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Numerical Approach to Double Well Problems
SIAM Journal on Numerical Analysis
Journal of the ACM (JACM)
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
On the effect of dissipation in shape-memory alloys
Nonlinear Analysis: Real World Applications
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
BEM solution of delamination problems using an interface damage and plasticity model
Computational Mechanics
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This paper presents an approach to numerical solution of problems posed in the framework of quasi-static rate-independent processes. As soon as a problem allows for an energetic formulation there are known methods of its time discretization by time incremental minimization problems, which demand for global optimization of a non-convex functional. Moreover the two-sided energy inequality, a necessary condition for optimization, can be formulated. Here we present an algorithm for finding solutions of rate-independent processes that verifies this condition and uses the strategy of backtracking if it is violated. We present the selectivity of the mentioned necessary condition in general and give numerical examples of the efficiency of such an algorithm, but also of situations that are beyond its limits. For those we propose a second strategy relying on wisely chosen combinations of spatial discretizations.