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Introduction to Numerical Analysis
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  • Cited by 50
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.
    Phong, Le Tien and Minh, Ngo Duc 2019. Advances in Engineering Research and Application. Vol. 63, Issue. , p. 63.
    Ahookhosh, Masoud and Neumaier, Arnold 2017. Solving structured nonsmooth convex optimization with complexity $$\mathcal {O}(\varepsilon ^{-1/2})$$ O ( ε - 1 / 2 ) . TOP,
    Bryla, Andrii 2017. Optimization Methods and Applications. Vol. 130, Issue. , p. 57.
    Ahookhosh, Masoud and Neumaier, Arnold 2017. Optimal subgradient algorithms for large-scale convex optimization in simple domains. Numerical Algorithms, Vol. 76, Issue. 4, p. 1071.
    Merlet, Jean-Pierre 2017. Simulation of Discrete-Time Controlled Cable-Driven Parallel Robots on a Trajectory. IEEE Transactions on Robotics, Vol. 33, Issue. 3, p. 675.
    Ahookhosh, Masoud and Neumaier, Arnold 2017. An optimal subgradient algorithm for large-scale bound-constrained convex optimization. Mathematical Methods of Operations Research, Vol. 86, Issue. 1, p. 123.
    Chen, Michael Mehrotra, Sanjay and Papp, Dávid 2015. Scenario generation for stochastic optimization problems via the sparse grid method. Computational Optimization and Applications, Vol. 62, Issue. 3, p. 669.
    Bosurgi, Gaetano D'Andrea, Antonino and Pellegrino, Orazio 2015. Interval analysis applied to road standard controls. Proceedings of the Institution of Civil Engineers - Transport, Vol. 168, Issue. 6, p. 543.
    Zhao, Qinghai Chen, Xiaokai Ma, Zheng-Dong and Lin, Yi 2015. Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties. Mathematical Problems in Engineering, Vol. 2015, Issue. , p. 1.
    Zare, A. R. and Ahmadizadeh, M. 2014. Design of viscous fluid passive structural control systems using pole assignment algorithm. Structural Control and Health Monitoring, Vol. 21, Issue. 7, p. 1084.
    Schichl, Hermann Markót, Mihály Csaba and Neumaier, Arnold 2014. Exclusion regions for optimization problems. Journal of Global Optimization, Vol. 59, Issue. 2-3, p. 569.
    Derkaoui, Orkia and Lehireche, Ahmed 2014. Safe Bounds in Semidefinite Programming by Using Interval Arithmetic. American Journal of Operations Research, Vol. 04, Issue. 05, p. 293.
    Lee, Chang Hyeong Shin, Jaemin and Kim, Junseok 2013. A numerical characteristic method for probability generating functions on stochastic first-order reaction networks. Journal of Mathematical Chemistry, Vol. 51, Issue. 1, p. 316.
    Kumar, Nitin and Rawat, Tarun Kumar 2012. Advances in Computer Science and Information Technology. Computer Science and Engineering. Vol. 85, Issue. , p. 32.
    Mohr, M. Biro, O. Stermecki, A. and Diwoky, F. 2012. An improved physical phase variable model for permanent magnet machines. p. 53.
    Schichl, Hermann and Markót, Mihály Csaba 2012. Algorithmic differentiation techniques for global optimization in the COCONUT environment. Optimization Methods and Software, Vol. 27, Issue. 2, p. 359.
    2011. Handbook of Monte Carlo Methods. p. 677.
    Bosurgi, Gaetano D’Andrea, Antonino and Pellegrino, Orazio 2011. CONTEXT SENSITIVE SOLUTIONS USING INTERVAL ANALYSIS / KONTEKSTINIAI SPRENDIMAI, NAUDOJANT INTERVALINĘ ANALIZĘ / КОНТЕКСТНЫЕ РЕШЕНИЯ С ИСПОЛЬЗОВАНИЕМ ИНТЕРВАЛЬНОГО АНАЛИЗА. TRANSPORT, Vol. 26, Issue. 2, p. 171.
    Neumaier, Arnold Fendl, Hannes Schilly, Harald and Leitner, Thomas 2011. VXQR: derivative-free unconstrained optimization based on QR factorizations. Soft Computing, Vol. 15, Issue. 11, p. 2287.
    Rump, Siegfried M. 2011. Verified bounds for singular values, in particular for the spectral norm of a matrix and its inverse. BIT Numerical Mathematics, Vol. 51, Issue. 2, p. 367.
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Book description

Numerical analysis is an increasingly important link between pure mathematics and its application in science and technology. This textbook provides an introduction to the justification and development of constructive methods that provide sufficiently accurate approximations to the solution of numerical problems, and the analysis of the influence that errors in data, finite-precision calculations, and approximation formulas have on results, problem formulation and the choice of method. It also serves as an introduction to scientific programming in MATLAB, including many simple and difficult, theoretical and computational exercises. A unique feature of this book is the consequent development of interval analysis as a tool for rigorous computation and computer assisted proofs, along with the traditional material.

Reviews

‘… very valuable in preparing a course on Numerical Analysis and is indeed very readable.’

Thomas Sonar Source: Zentralblatt MATH

‘This is very strongly recommended reading for all undergraduate students whose courses require a serious understanding and implementation of numerical analysis.‘

Source: The Mathematical Gazette

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