Vassilios S. Vassiliadis is a Senior Lecturer in the Department of Chemical Engineering at the University of Cambridge. He is also the CEO and CTO of the spin-out company, Cambridge Simulation Solutions LTD. Walter Kähm, a former PhD student under Vassilios S. Vassiliadis, is a process engineer in the chemical sector. Ehecatl Antonio del Rio-Chanona is a lecturer and head of the optimization and machine learning for the process systems engineering group in the Department of Chemical Engineering and the Centre for Process Systems Engineering (CPSE) at Imperial College London. Ye Yuan is currently a professor at Huazhong University of Science and Technology.

'This book offers a very clear, uncluttered presentation of key ideas of optimisation in rigorous form and with plenty of examples from a decade of research and educational experience. It offers an exceptional resource for educators and students of optimisation methods, as well as a valuable reference text to practitioners.' Alexei Lapkin, University of Cambridge

• Part I. Overview of Optimization: 1. Introduction to optimization; Part II. From General Mathematical Background to General Nonlinear Programming Problems (NLP): 2. General concepts; 3. Convexity; 4. Quadratic functions; 5. Minimization in one dimension; 6. Unconstrained multivariate gradient-based minimization; 7. Constrained nonlinear programming problems (NLP); 8. Penalty and barrier function methods; 9. Interior point methods (IPMs), a detailed analysis; Part III. Formulation and Solution of Linear Programming (LP) Problem Models: 10. Introduction to LP models; 11. Numerical solution of LP problems using the simplex method; 12. A sampler of LP problem formulations; 13. Regression revisited, using LP to fit linear models; 14. Network flow problems; 15, LP and sensitivity analysis, in brief; Part IV. Further Topics in Optimization: 16. Multiobjective optimilzation problem (MOP); 17. Stochastic optimization problem (SOP); 18. Mixed integer programming; 19. Global optimization; 20. Optical control problems (dynamic optimization); 21. System identification and model predictive control.