On the Computational Cost and Complexity of Stochastic Inverse Solvers

@article{Faliszewski2016OnTC,
  title={On the Computational Cost and Complexity of Stochastic Inverse Solvers},
  author={P. Faliszewski and M. Smolka and R. Schaefer and M. Paszyński},
  journal={Comput. Sci.},
  year={2016},
  volume={17},
  pages={225-264}
}
The goal of this paper is to provide a starting point for investigations into a mainly underdeveloped area of research regarding the computational cost analysis of complex stochastic strategies for solving parametric inverse problems. This area has two main components: solving global optimization problems and solving forward problems (to evaluate the misfit function that we try to minimize). For the first component, we pay particular attention to genetic algorithms with heuristics and to multi… Expand
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References

SHOWING 1-10 OF 90 REFERENCES
A hybrid algorithm for solving inverse problems in elasticity
TLDR
A new approach to handling difficult parametric inverse problems in elasticity and thermo-elasticity, formulated as global optimization ones, which allows common scaling of the accuracy of solving forward and inverse problems, which is the core of the introduced double-adaptive technique. Expand
Multi-deme, twin adaptive strategy hp-HGS
The article presents a twin adaptive, effective stochastic strategy for solving difficult inverse problems formulated as global optimization ones. It is especially dedicated to multimodal, noisyExpand
Handbook of global optimization
Preface. 1. Tight relaxations for nonconvex optimization problems using the Reformulation-Linearization/Convexification Technique (RLT) H.D. Sherali. 2. Exact algorithms for global optimization ofExpand
On the complexity of local search
TLDR
The main results are these: Finding a local optimum under the Lin-Kernighan heuristic for the traveling salesman problem is PLS-complete, and a host of simple unweighted local optimality problems are P-complete. Expand
How easy is local search?
TLDR
A natural class PLS is defined consisting essentially of those local search problems for which local optimality can be verified in polynomial time, and it is shown that there are complete problems for this class. Expand
Multimodal function optimization with a niching genetic algorithm: A seismological example
Abstract We present a variant of a traditional genetic algorithm, known as a niching genetic algorithm (NGA), which is effective at multimodal function optimization. Such an algorithm is useful forExpand
The island model as a Markov dynamic system
TLDR
A mathematical framework describing a wide class of island-like strategies as a stationary Markov chain as well as the mechanism of inter-deme agent operation synchronization is proposed. Expand
Delta Coding: An Iterative Search Strategy for Genetic Algorithms
A new search strategy for genetic algorithms is introduced which allows iterative searches with complete reinitialization of the population preserving the progress already made toward solving anExpand
The influence of migration sizes and intervals on island models
TLDR
It is observed that even small migrations already make a significant impact on the behavior of an island model and therefore the effects are comparable to those of bigger migrations, while rare migrations cause a degraded performance due to the slow convergence. Expand
Optimizing Monotone Functions Can Be Difficult
TLDR
This is the first time that a constant factor change of the mutation probability changes the run-time by more than constant factors. Expand
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