This monograph presents a fresh perspective on Unconstrained Optimization by integrating quantum calculus with single-objective and multiobjective optimization. It introduces the fundamental concepts of quantum calculus and develops their application to several optimization methods.
The book presents novel quantum-calculus-based techniques for solving single-objective optimization problems and extends these ideas to multiobjective optimization involving competing objectives. Through mathematical formulations, algorithmic developments, and computational examples, it provides new insights into the theory and practice of optimization.
By connecting quantum calculus with different optimization paradigms, the book offers a distinctive framework for researchers, postgraduate students, and practitioners interested in optimization, computational mathematics, and emerging quantum-inspired methodologies.
This monograph presents a fresh perspective on Unconstrained Optimization by integrating quantum calculus with single-objective and multiobjective optimization. It introduces the fundamental concepts of quantum calculus and develops their application to several optimization methods.
The book presents novel quantum-calculus-based techniques for solving single-objective optimization problems and extends these ideas to multiobjective optimization involving competing objectives. Through mathematical formulations, algorithmic developments, and computational examples, it provides new insights into the theory and practice of optimization.
By connecting quantum calculus with different optimization paradigms, the book offers a distinctive framework for researchers, postgraduate students, and practitioners interested in optimization, computational mathematics, and emerging quantum-inspired methodologies.
Bhagwat Ram
Multiobjective optimization Quantum calculus Quantum derivative Quantum Pareto optimal Gradient descent BFGS method Quantum Inspired Newton’s method