Historical Note on Unconstrained Optimization and Quantum Calculus -- Basics of Unconstrained Optimization and Quantum Calculus -- Quantum-Steepest Descent Method with Quasi-Fejér Convergence -- Quantum- Fletcher-Reeves Conjugate Gradient Method -- Quantum-Polak-Ribière-Polyak -- Conjugate Gradient Method.-Quantum-Dai-Yuan Conjugate Gradient Method -- Quantum-Broyden-Fletcher-Goldfarb-Shanno Method -- Quantum-Limited Memory Broyden-Fletcher-Goldfarb-Shanno Method.

This book provides a better clue to apply quantum derivative instead of classical derivative in the modified optimization methods, compared with the competing books which employ a number of standard derivative optimization techniques to address large-scale, unconstrained optimization issues. Essential proofs and applications of the various techniques are given in simple manner without sacrificing accuracy. New concepts are illustrated with the help of examples. This book presents the theory and application of given optimization techniques in generalized and comprehensive manner. Methods such as steepest descent, conjugate gradient and BFGS are generalized and comparative analyses will show the efficiency of the techniques.