  With the development of machine learning and Big Data, the concepts of linear
and non-linear optimization techniques are becoming increasingly valuable for
many quantitative disciplines. Problems of that nature are typically solved
using distinctive optimization algorithms, iterative methods, or heuristics. A
new variant of the Accelerated Projection-Based Consensus (APC) iterative
method is proposed, which is faster than its classical version while handling
large sparse matrices in distributed settings. The algorithm is proposed, and
its description and implementation in a high-level programming language are
presented. Convergence tests measuring acceleration factors based on real-world
datasets are done, and their results are promising. The results of this
research can be used as an alternative to solving numerical optimization
problems.
