2017/09/07 by Mark Amo-Boateng, Amo-Boateng, Mark · 1 voice
Computer Science · Mathematics · #Algorithms and Data Compression #Computational Complexity (cs.CC) #Data Structures and Algorithms (cs.DS) #Distributed #FOS: Computer and information sciences #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Optimization and Control (math.OC) #Parallel #Parallel Computing and Optimization Techniques #Performance (cs.PF) #and Cluster Computing (cs.DC) #cs.CC #cs.DC #cs.DS #cs.PF #math.OC
paper · pdf · doi:10.48550/arxiv.1709.02500
7 pages, 4 figures, 7 tables
openalex publication_date 2017/09/07 · arxiv published 2017/09/08 · arxiv created 2017/09/11 · arxiv updated 2017/09/12 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
This article presents the novel breakthrough general purpose algorithm for large scale optimization problems. The novel algorithm is capable of achieving breakthrough speeds for very large-scale optimization on general purpose laptops and embedded systems. Application of the algorithm to the Griewank function was possible in up to 1 billion decision variables in double precision took only 64485 seconds (~18 hours) to solve, while consuming 7,630 MB (7.6 GB) or RAM on a single threaded laptop CPU. It shows that the algorithm is computationally and memory (space) linearly efficient, and can find the optimal or near-optimal solution in a fraction of the time and memory that many conventional algorithms require. It is envisaged that this will open up new possibilities of real-time large-scale problems on personal laptops and embedded systems.