. "Mathematical optimization"@en . . "6.0" . "Optimization (or “Optimisation”) is the subject of finding the best or optimal solution to a problem from a set of potential or feasible solutions.\nOptimization problems are fundamental in all forms of decision-making, since one wishes to make the best decision in any context, and in the analysis of data, where one wishes to find the best model describing experimental data. This course treats two different areas of optimization: nonlinear optimization and combinatorial optimization. Nonlinear optimization deals with the situation that there is a\ncontinuum of available solutions. A best solution is then usually approximated with one of several available general-purpose algorithms, such as Brent’s method for one-dimensional problems, Newton, quasi-Newton and conjugate gradient methods for unconstrained problems, and Lagrangian methods, including active-set methods, sequential quadratic programming and interior-point methods for general constrained problems. Combinatorial optimization deals with situations that a best solution from a finite number of available solutions must be chosen. A variety of techniques, such as linear programming, branch and cut, Lagrange relaxation dynamic programming and approximation algorithms are employed to tackle this type of problems. Throughout the course, we aim to provide a coherent framework for the subject, with a focus on consideration of optimality conditions (notably the Karush-Kuhn-Tucker conditions), Lagrange multipliers and duality, relaxation and approximate problems, and on convergence rates and computational complexity.\nThe methods will be illustrated by in-class computer demonstrations, exercises illustrating the main concepts and algorithms, and modelling and computational work on case studies of practical interest, such as optimal control and network flow.\n\nPrerequisites\nDesired Prior Knowledge: Simplex algorithm. Calculus, Linear Algebra.\n\nRecommended reading\n1. Nonlinear Programming, Theory and Algorithms, by Bazaraa, Sherali, and Shetty (Wiley). 2. Combinatorial Optimization, Algorithm and Complexity, by Papadimitriou and Steiglitz (Dover Publications).\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/464091/mathematical-optimization" . . "Presential"@en . "FALSE" . . "Mathematical Optimisation"@en . . . . . . . . . . . . . . . "Master in Data Science for Decision Making"@en . . "https://curriculum.maastrichtuniversity.nl/education/partner-program-master/data-science-decision-making" . "120"^^ . "Presential"@en . "Data Science for Decision Making will familiarise you with methods, techniques and algorithms that can be used to address major issues in mathematical modelling and decision making. You will also get hands-on experience in applying this knowledge through computer classes, group research projects and the thesis research. The unique blend of courses will equip you with all the knowledge and skills you’ll need to have a successful career.\n\nWidespread applications\nData Science for Decision Making links data science with making informed decisions. It has widespread applications in business and engineering, such as scheduling customer service agents, optimising supply chains, discovering patterns in time series and data, controlling dynamical systems, modelling biological processes, finding optimal strategies in negotiation and extracting meaningful components from brain signals. This means you'll be able to pursue a career in many different industries after you graduate.\n\nProgramme topics\nData Science for Decision Making covers the following topics:\n\n* production planning, scheduling and supply chain optimisation\n* modelling and decision making under randomness, for instance in queuing theory and simulation\n* signal and image processing with emphasis on wavelet analysis and applications in biology\n* algorithms for big data\n* estimation and identification of mathematical models, and fitting models to data\n* dynamic game theory, non-cooperative games and strategic decision making with applications in evolutionary game theory and biology\n* feedback control design and optimal control, for stabilisation and for tracking a desired behaviour\n* symbolic computation and exact numerical computation, with attention to speed, efficiency and memory usage\n* optimisation of continuous functions and of problems of a combinatorial nature"@en . . . "2"@en . "FALSE" . . "Master"@en . "Thesis" . "2314.00" . "Euro"@en . "18400.00" . "Recommended" . "Data science and big data are very important to companies nowadays, and this programme will provide you with all the training you’ll need be active in these areas. The comprehensive education, practical skills and international orientation of the programme will open the world to you. When applying for positions, graduates from Data Science for Decision Making are often successful because of their problem-solving attitude, their modern scientific skills, their flexibility and their ability to model and analyse complex problems from a variety of domains.\n\nGraduates have found positions as:\n* Manager Automotive Research Center at Johnson Electric\n* Creative Director at Goal043 | Serious Games\n* Assistant Professor at the Department of Advanced Computing Sciences, Maastricht University\n* BI strategy and solutions manager at Vodafone Germany\n* Scientist at TNO\n* Digital Analytics Services Coordinator at PFSweb Europe\n* Software Developer at Thunderhead.com\n* Data Scientist at BigAlgo\n* Researcher at Thales Nederland"@en . "2"^^ . "TRUE" . "Midstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . .