Numerical Techniques for Stochastic Optimization by Yuri Ermoliev
Rapid changes in today's environment emphasize the need for models and meth- ods capable of dealing with the uncertainty inherent in virtually all systems re- lated to economics, meteorology, demography, ecology, etc. Systems involving interactions between man, nature and technology are subject to disturbances which may be unlike anything which has been experienced in the past. In the technological revolution increases uncertainty-as each new stage particular, perturbs existing knowledge of structures, limitations and constraints. At the same time, many systems are often too complex to allow for precise measure- ment of the parameters or the state of the system. Uncertainty, nonstationarity, disequilibrium are pervasivE' characteristics of most modern systems. In order to manage such situations (or to survive in such an environment) we must develop systems which can facilitate oar response to uncertainty and changing conditions. In our individual behavior we often follow guidelines that are conditioned by the need to be prepared for all (likely) eventualities: insur- ance, wearing seat-belts, savings versus investments, annual medical check.ups, even keeping an umbrella at the office, etc. One can identify two major types of mechanisms: the short term adaptive adjustments (defensive driving, mar- keting, inventory control, etc.) that are made after making some observations of the system's parameters, and the long term anticipative actions (engineer- ing design, policy setting, allocation of resources, investment strategies, etc.).-
Numerical Analysis for Elliptic Optimal Control Problems
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Introduction to Shape Optimization
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Numerical Methods for Two-phase Incompressible Flows
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Time-Domain Finite Element Methods for Maxwell's Equations in Metamaterials
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Spectral Methods
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The Graduate Student's Guide to Numerical Analysis '98
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The Linearization Method for Constrained Optimization
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Hilbert Space Splittings and Iterative Methods
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The Concept of Stability in Numerical Mathematics
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Monotone Discretizations for Elliptic Second Order Partial Differential Equations
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Boundary Element Methods
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History of Continued Fractions and Padé Approximants
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Method of Difference Potentials and Its Applications
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Newton Methods for Nonlinear Problems
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High Order Difference Methods for Time Dependent PDE
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Logarithmic Norms
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Minimization Methods for Non-Differentiable Functions
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Numerical Toolbox for Verified Computing I
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Hierarchical Matrices: Algorithms and Analysis
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Mixed and Hybrid Finite Element Methods
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Solving Elliptic Problems Using ELLPACK
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Discrete Iterations
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Numerical Continuation Methods
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Krylov Methods for Nonsymmetric Linear Systems
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Progress in Approximation Theory
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Sequence Transformations
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Nonlinear Approximation Theory
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Moduli of Smoothness
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Numerical Methods Based on Sinc and Analytic Functions
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Krylov Subspace Methods for Linear Systems
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Advanced Boundary Element Methods
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Retarded Potentials and Time Domain Boundary Integral Equations
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Numerical Modeling in Materials Science and Engineering
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Robust Numerical Methods for Singularly Perturbed Differential Equations
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Finite Element Methods for Incompressible Flow Problems
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Solving Ordinary Differential Equations I
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Solving Ordinary Differential Equations II
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Tensor Spaces and Numerical Tensor Calculus
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Matrix Iterative Analysis
| SKU | Unavailable |
| ISBN 13 | 9783642648137 |
| ISBN 10 | 3642648134 |
| Title | Numerical Techniques for Stochastic Optimization |
| Author | Yuri Ermoliev |
| Series | Springer Series In Computational Mathematics |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer |
| Year published | 2011-10-04 |
| Number of pages | 571 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |