Algorithms for encoding and decoding information play a critical role in the optimization of modern systems, enabling efficient data representation, transmission, storage, and retrieval. This paper explores a landscape of encoding and decoding algorithms in payment systems with a focus on enhancing system performance across diverse domains, including communication networks, distributed computing, and machine learning. We analyze lossless and lossy encoding techniques, discussing their trade-offs in terms of compression ratio, computational complexity, and resilience to errors. Special attention is given to entropy-based methods such as Huffman and arithmetic coding, as well as modern techniques like neural compression and error-correcting codes including LDPC and Reed–Solomon codes. Furthermore, we investigated adaptive and context-aware encoding schemes that dynamically adjust to data patterns and system requirements, contributing to real-time optimization. By integrating these algorithms within larger system architectures, substantial gains in throughput, latency, and energy efficiency are demonstrated. The paper concludes with a discussion on current challenges, including scalability and security, and proposes future research directions that leverage advances in artificial intelligence and quantum computing to further enhance encoding and decoding strategies for next-generation system optimization. Keywords: Algorithms, Encoding, Decoding, Compression, Adaptive Systems
Ogbodo et al. (Mon,) studied this question.