The paper proposes an advanced inertial forward-backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence result...
详细信息
The paper proposes an advanced inertial forward-backward splitting algorithm in combination with a parallel hybrid method for approximating solutions of common variational inclusion problems. Strong convergence results have been obtained in real Hilbert spaces subject to certain suitable conditions. Applications and numerical results have also been incorporated to justify the applicability of our findings as well as comparability by exhibiting a better rate of convergence by our proposed algorithm than several other well-known algorithms. Further, we solve unconstrained image recovery problems and the quality of the proposed algorithm has also been demonstrated for common types of blur effects.
作者：
Nuel, G.Sorbonne Univ
Natl Inst Math Sci INSMI Probabil & Stat LPSM CNRS 8001LPMACNRS 7599 F-75005 Paris France
We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding ...
详细信息
We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding techniques allow reducing the problem to the count of specific transitions in a (heterogenous) order 1 Markov chain over a deterministic finite automaton state space. From this result we derive the expression of both the mgf/pgf of N as well as the factorial and non-factorial moments of N. We then introduce the notion of evidence-based constraints in this context. Following the classical forward/backward algorithm in hidden Markov models, we provide explicit recursions allowing to compute the mgf/pgf of N under the evidence constraint. All the results presented are illustrated with a toy example.
暂无评论