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Architecture --- architectuur --- Delft University of Technology

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This year, 2020, the Faculty of Aerospace Engineering at Delft University of Technology in the Netherlands celebrates its 80th birthday. This paper describes the history of the department since its founding in early 1940, just before the start of World War II in the Netherlands, until present day. The paper will highlight how its research and education developed within the socio-economic context of the Netherlands and the developments in aerospace over the past 80 years.

Civil service & public sector --- Aerospace Engineering --- History --- Delft University of Technology

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Architecture --- Schools of architecture --- Schools of architecture. --- Study and teaching --- Study and teaching. --- Delft University of Technology. --- Netherlands.

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Fuel cell cars can provide more efficient and cleaner transportation. However, we use our cars for transportation only 5% of the time. When parked, the fuel cell in the car can produce electricity from hydrogen, which is cleaner and more efficient than the current electricity system, generating useful ‘waste’ products in the form of heat and fresh water. The produced electricity, heat and fresh water can be fed into the respective grids or be used directly in our house, office or the school of our kids. The required hydrogen can be produced from gas (natural gas, biogas) or electricity (hydro, wind, solar, etc.). In the end, these fuel cell cars can replace all power plants worldwide. As a result, the ‘car as power plant’ can create an integrated, efficient, reliable, flexible, clean, smart and personalized transport, energy and water system: a real paradigm shift. The ‘Car as Power Plant’ is developed at Delft Technical University, in The Green Village: a sustainable, lively and entrepreneurial environment where we discover, learn and show how to solve society’s urgent challenges. The Green Village unifies clever, imaginative strengths of scientists and entrepreneurs and turns ideas and visions into experiences and commercially viable products and services. Innovative power that sets horizons for a new, sustainable, green and circular economy.

Renewable energy sources --- Automobiles --- Mechanical Engineering --- Engineering & Applied Sciences --- Mechanical Engineering - General --- Technological innovations --- Design and construction --- Environmental aspects --- Autos (Automobiles) --- Cars (Automobiles) --- Gasoline automobiles --- Motorcars (Automobiles) --- Automobile engineering --- Automotive engineering --- Alternate energy sources --- Alternative energy sources --- Energy sources, Renewable --- Sustainable energy sources --- Construction --- Renewable energy sources. --- Technological innovations. --- Design and construction. --- Environmental aspects. --- Power resources --- Renewable natural resources --- Agriculture and energy --- renewable energy --- The Green Village --- Delft University of Technology --- TU Delft --- fuel cell cars --- sustainability

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Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function.The authors deal with linear optimization, nonlinear complementarity problems, semidefinite optimization, and second-order conic optimization problems. The framework also covers large classes of linear complementarity problems and convex optimization. The algorithm considered can be interpreted as a path-following method or a potential reduction method. Starting from a primal-dual strictly feasible point, the algorithm chooses a search direction defined by some Newton-type system derived from the self-regular proximity. The iterate is then updated, with the iterates staying in a certain neighborhood of the central path until an approximate solution to the problem is found. By extensively exploring some intriguing properties of self-regular functions, the authors establish that the complexity of large-update IPMs can come arbitrarily close to the best known iteration bounds of IPMs.Researchers and postgraduate students in all areas of linear and nonlinear optimization will find this book an important and invaluable aid to their work.

Interior-point methods. --- Mathematical optimization. --- Programming (Mathematics). --- Mathematical optimization --- Interior-point methods --- Programming (Mathematics) --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Operations research --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Simulation methods --- System analysis --- 519.85 --- 681.3*G16 --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- 519.85 Mathematical programming --- Accuracy and precision. --- Algorithm. --- Analysis of algorithms. --- Analytic function. --- Associative property. --- Barrier function. --- Binary number. --- Block matrix. --- Combination. --- Combinatorial optimization. --- Combinatorics. --- Complexity. --- Conic optimization. --- Continuous optimization. --- Control theory. --- Convex optimization. --- Delft University of Technology. --- Derivative. --- Differentiable function. --- Directional derivative. --- Division by zero. --- Dual space. --- Duality (mathematics). --- Duality gap. --- Eigenvalues and eigenvectors. --- Embedding. --- Equation. --- Estimation. --- Existential quantification. --- Explanation. --- Feasible region. --- Filter design. --- Function (mathematics). --- Implementation. --- Instance (computer science). --- Invertible matrix. --- Iteration. --- Jacobian matrix and determinant. --- Jordan algebra. --- Karmarkar's algorithm. --- Karush–Kuhn–Tucker conditions. --- Line search. --- Linear complementarity problem. --- Linear function. --- Linear programming. --- Lipschitz continuity. --- Local convergence. --- Loss function. --- Mathematician. --- Mathematics. --- Matrix function. --- McMaster University. --- Monograph. --- Multiplication operator. --- Newton's method. --- Nonlinear programming. --- Nonlinear system. --- Notation. --- Operations research. --- Optimal control. --- Optimization problem. --- Parameter (computer programming). --- Parameter. --- Pattern recognition. --- Polyhedron. --- Polynomial. --- Positive semidefinite. --- Positive-definite matrix. --- Quadratic function. --- Requirement. --- Result. --- Scientific notation. --- Second derivative. --- Self-concordant function. --- Sensitivity analysis. --- Sign (mathematics). --- Signal processing. --- Simplex algorithm. --- Simultaneous equations. --- Singular value. --- Smoothness. --- Solution set. --- Solver. --- Special case. --- Subset. --- Suggestion. --- Technical report. --- Theorem. --- Theory. --- Time complexity. --- Two-dimensional space. --- Upper and lower bounds. --- Variable (computer science). --- Variable (mathematics). --- Variational inequality. --- Variational principle. --- Without loss of generality. --- Worst-case complexity. --- Yurii Nesterov. --- Mathematical Optimization --- Mathematics --- Programming (mathematics)