About this book
Complete unit-wise notes for Optimization Techniques (AIDS213), the GGSIPU / USAR third-semester paper for B.Tech AI & Data Science. 23 pages, prepared from the official syllabus approved by the Board of Studies on 16 June 2026. Unit I — Line Search Methods for Unconstrained Optimization: optimality conditions, unimodal functions, Golden Section Search, Fibonacci Search, and the comparison between them. Unit II — Gradient-Based Unconstrained Optimization: multivariable optimality conditions, the descent framework, Steepest Descent (Cauchy), Newton's Method, and Conjugate Gradient (Fletcher–Reeves). Unit III — Numerical Techniques for Constrained Optimization: formulation, the SUMT idea, exterior penalty functions and interior barrier functions, with the trade-off between them. Unit IV — Multi-Objective Optimization: conflicting objectives, dominance and Pareto optimality, the efficient frontier, and the weighted sum approach. Opens with a preliminaries section on mathematical modelling and problem classification, and carries worked numericals throughout — rounded to three decimals, so check the arithmetic when you reproduce them in the exam.