One of the main reasons for using parallel evolutionary algorithms (PEAs) is to obtain efficient algorithms with an execution time much lower than that of their sequential counterparts in order, e.g., to tackle more complex problems. This naturally leads to measuring the speedup of the PEA. PEAs have sometimes been reported to provide super-linear performances for different problems, parameteri...

The solution of large, sparse systems of linear equations is an inherent part of many computational methods in science and engineering. For such systems, iterative methods are often more attractive than direct methods because of their small (and constant) memory requirements. Also, the performance of iterative solvers can easily be improved by using distributed systems. A common performance cha...

In this paper, we investigate the quality of several linear-time schedulability tests for preemptive and non-preemptive fixed-priority scheduling of uniprocessor systems. The metric used to assess the quality of these tests is the resource augmentation bound commonly known as the processor speedup factor. The speedup factor of a schedulability test corresponds to the smallest factor by which th...

This paper presents a quantum-assisted index modulation for next-generation IoT wireless networks. The NP-hard selection problem is first formulated by quadratic unconstrained binary optimization (QUBO) consisting of constraints feasible solutions. To minimize the number qubits required quantum circuit, this formulation then simplified dictionary-based approach that partially exploits classical...

Parallelizing sparse simplex algorithms is one of the most challenging problems. Because of very sparse matrices and very heavy communication, the ratio of computation to communication is extremely low. It becomes necessary to carefully select parallel algorithms , partitioning patterns, and communication optimization to achieve a speedup. Two implementations on Intel hypercubes are presented i...

We describe Asaga, an asynchronous parallel version of the incremental gradient algorithm Saga that enjoys fast linear convergence rates. Through a novel perspective, we revisit and clarify a subtle but important technical issue present in a large fraction of the recent convergence rate proofs for asynchronous parallel optimization algorithms, and propose a simplification of the recently introd...