DETERMINISTIC SUSTAINABLE COST-EFFICIENT ARTIFICIAL INTELLIGENCE: A Complementary Computational Framework

Deterministic Sustainable Cost-Efficient Artificial Intelligence presents a complementary deterministic (non-iterative) perspective on artificial intelligence. Rather than treating learning exclusively as an iterative optimization process, this book explores conditions under which learning may be formulated as a structural equilibrium problem and computed directly through algebraic methods. The book develops the Cekirge σ-Regularized Framework, a deterministic approach in which learning is obtained through closed-form computation instead of repeated gradient-based optimization. Topics include deterministic learning, perturbation analysis, structural equilibrium, transformer architectures, regularization, scalability, energy-efficient computation, and deterministic inference. Rather than replacing existing optimization-based methods, this work introduces a complementary mathematical framework intended to stimulate discussion, further research, and experimental validation. The book is intended for researchers, graduate students, engineers, and professionals working in artificial intelligence, machine learning, computational mathematics, optimization, and sustainable computing.