Focusing on the interconnectedness of algorithms in nonlinear programming and variational inequality problems, this book introduces the Cost Approximation framework. It presents a systematic approach to understanding algorithms through approximating mappings, iteration updates, and a merit function for monitoring convergence. By consolidating diverse methods, it offers an intuitive tool for comparing and relating algorithms, maintaining a level of detail that aligns with the original problem statements. This work aims to enhance comprehension and facilitate connections within the field.
