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Tuesday, April 28, 2020 | History

3 edition of Uncertain input data problems and the worst scenario method found in the catalog.

Uncertain input data problems and the worst scenario method

Ivan HlavГЎДЌek

Uncertain input data problems and the worst scenario method

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  • 30 Currently reading

Published by Elsevier in Amsterdam, Boston .
Written in English

    Subjects:
  • Uncertainty (Information theory),
  • Error analysis (Mathematics),
  • Mathematical models.

  • Edition Notes

    Includes bibliographical references (p. 427-448) and index.

    StatementIvan Hlaváček, Jan Chleboun, Ivo Babuška.
    SeriesNorth-Holland series in applied mathematics and mechanics -- v. 46
    ContributionsChleboun, Jan., Babuška, Ivo.
    Classifications
    LC ClassificationsQ375 .H53 2004
    The Physical Object
    Paginationxxvi, 458 p. :
    Number of Pages458
    ID Numbers
    Open LibraryOL22632632M
    ISBN 10044451435X

    This sort of examination of the impact of the input data on the output results is crucial. The procedures and algorithms of mathematical programming are important, but the problems that really bedevil you in practice are usually associated with data: getting it at all, and getting accurate data. Some data, necessary for your mathematical model, is.


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Uncertain input data problems and the worst scenario method by Ivan HlavГЎДЌek Download PDF EPUB FB2

Purchase Uncertain Input Data Problems and the Worst Scenario Method, Volume 46 - 1st Edition. Print Book & E-Book. ISBNUncertain Input Data Problems and the Worst Scenario Method (ISSN Book 46) - Kindle edition by Hlavacek, Ivan, Chleboun, Jan, Babuska, Ivo, Achenbach, Jan.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Uncertain Input Data Problems and the Worst Scenario Method (ISSN Book 46).Manufacturer: Elsevier Science.

Uncertain Input Data Problems and the Worst Scenario Method (Volume 46) (North-Holland Series in Applied Mathematics and Mechanics Uncertain input data problems and the worst scenario method book 46)) 1st Edition by Ivan Hlavacek (Author), Jan Chleboun (Author), Ivo Babuska (Author), Jan Achenbach (Editor) & 1 moreCited by: Uncertain Input Data Problems and the Worst Scenario Method.

Edited by Ivan Hlaváěek, Jan Chleboun, Ivo Babuška. Vol General Abstract Scheme and the Analysis of the Worst Scenario Method.

(04) Book chapter Full text access Chapter II - General Abstract Scheme and the Analysis of the Worst Scenario Method. Ivan. Uncertain input data problems and the worst scenario method book Get this from a library. Uncertain input data problems and the worst scenario method.

[Ivan Hlaváček; Jan Chleboun; Ivo Babuška] Uncertain input data problems and the worst scenario method book This book deals with the impact of uncertainty in input data on the outputs of mathematical models.

Uncertain inputs as scalars, tensors, functions, or domain boundaries are considered. In practical. An introduction to the worst scenario method is given. We start with an example and a general abstract scheme. An analysis of the method both on the continuous and approximate levels is discussed. We show a possible incorporation of the method into the fuzzy set theory.

Finally, we present a survey of applications published during the last by: Download Citation | Uncertain input data problems and the worst scenario method | An introduction to the worst scenario method is given. We start with an Uncertain input data problems and the worst scenario method book and a general abstract scheme.

An Author: Ivan Hlaváček. In practical terms, material parameters or constitutive laws, for instance, are uncertain, and quantities as local temperature, local mechanical stress, or local displacement are monitored.

The goal of the worst scenario method is to extremize the quantity over the set of uncertain input data. ISBN: X OCLC Number: Description: xxvi, pages: illustrations ; 24 cm. Contents: I. Reality, mathematics, and computation l abstract scheme and the analysis of the worst scenario method inear elliptic boundary value problems lic problems --V.

Elastic and thermoelastic beams c plates and pseudoplates --VII. Read "Uncertain Input Data Problems and the Worst Scenario Method" by Ivan Hlavacek available from Rakuten Kobo. This book deals with the impact of uncertainty in input data on the outputs of mathematical models.

Uncertain inputs as Brand: Elsevier Science. The first step in the process of structuring scenarios is to develop an event sequence diagram describing “the success” or “as planned,” scenario (S 0).This scenario, after going through N events, leads to the successful, or “as planned” end-state, denoted ES describes the normal operating procedures for the system in the absence of any undesired initiating events (IEs).

Abstract. Three ingredients constitute mathematical models dependent on parameters whose value is uncertain: a compact set u ad of admissible parameters a, a state problem A(a)u = f(a) with an a-dependent state u ≡ u(a), and a continuous quantity of interest Ψ(a) = Φ(a,u(a)).In the worst scenario method (WSM), the maximum of Ψ over u ad is : J.

Chleboun. In addition to probabilistic uncertainty, this book also deals with ellipsoidal and more general convex descriptions of uncertainty. Uncertain Input Data Problems and the Worst Scenario Method, by Ivan Hlavacek, Jan Chleboun, and Ivo Babuska, Elsevier, Amsterdam,ISBN X.

In numerical analysis, the interval finite element method (interval FEM) is a finite element method that uses interval parameters. Interval FEM can be applied in situations where it is not possible to get reliable probabilistic characteristics of the structure.

This is important in concrete structures, wood structures, geomechanics, composite structures, biomechanics and in many other areas. Product Type: Book Edition: 1 Volume: 45 First Published: eBook: On Topological Derivatives for Elastic Solids with Uncertain Input Data Article (PDF Available) in Journal of Optimization Theory and Applications (3) June with 48 Reads.

Uncertain Input Data Problems and the Worst Scenario Method, () Unilateral Contact with Coulomb Friction and Uncertain Input Data. Numerical Functional Analysis and Optimization 24 Cited by: The finite element method is a numerical method widely used in engineering.

Experience shows that unreliable computation can lead to very serious consequences. Hence reliability questions stand more and more at the forefront of engineering and theoretical interests.

The present book presents the mathematical theory of the finite element method and focuses on the question of how reliable. Uncertain Input Data Problems and the Worst Scenario Method Ivan Hlavacek, Jan Chleboun, Ivo Babuska, Jan Achenbach. With this assumption, the value of the left hand side of the \true" constraint i x X j eaijxj bi 0 at a candidate solution xis a random variable with mean and the standard deviation given by Mean(ix) = X j aijxj 2bi; StD(i x) = ˙iDi(x); D i (x) = sX j2Ji a2 ijxj; where Ji is the set of indices of uncertain data entries of the i-th constraint and ˙i is the standard deviation of ˘ij.

In a worst-case scenario, management becomes a slave to historical data and trends rather than worrying about what the business is doing now.

Scenario Planning is a method of direction-finding and strategy formation that defines itself by non-prediction. Scenarios are integrated narratives of how the future may unfold, with always two. () Robust Estimation for Uncertain Models in a Data Fusion Scenario. IFAC Proceedings Volumes() Parameter estimation with Cited by: Uncertain Input Data Problems and the Worst Scenario Method, Volume 46(1st Edition) (North-Holland Series in Applied Mathematics and Mechanics) by Jan Chleboun, Ivan Hlavacek, Ivo Babuska, Jan Achenbach, Ivan Hlaváček, Ivo Babuška Hardcover, Pages, Published by Elsevier Science ISBNISBN: X.

Studies DeutschesInstitutfürEntwicklungspolitik 39 Methodsoffutureandscenarioanalysis Overview,assessment,andselectioncriteria HannahKosow RobertGaßnerFile Size: 1MB. Scenario planning, also called scenario thinking or scenario analysis, is a strategic planning method that some organizations use to make flexible long-term plans.

It is in large part an adaptation and generalization of classic methods used by military intelligence. The original method was that a group of analysts would generate simulation games for policy makers.

Data searches that yield incorrect results - This means that a search retrieves incorrect data as the result of a search.

In the worst case situation, the data retrieved appears to be correct in format, but only by tracing back to source documents and other original data can it be determined that the data is incorrect for the search criteria.

Think about worst-case scenarios first Scenario thinking sketches out future cybersecurity problems and helps policymakers begin addressing tomorrow's. Robust solutions to uncertain weighted least squares problems Many real-world optimization problems involve input data that is noisy or uncer-tain, due to measurement or modelling errors, or simply the unavailability of the Robust solutions to uncertain weighted least squares problems with kqk = by: 2.

methods with which uncertainty is included when analyzing data. For data analysis of the type we will be doing in Physicsan effective analysis method is called Worst Case Analysis. Completion of Worst Case Analysis yields the best value of the result and a range of uncertainty in the form best valueFile Size: 22KB.

Energy Scenarios: The Value and Limits of Scenario Analysis. Sergey Paltsev* Massachusetts Institute of Technology, Cambridge, MA, USA. Abstract. A need for a low-carbon world has added a new challenging dimension for the longterm - energy scenarios development. In addition to the traditional factors like technologicalFile Size: 1MB.

Sampled data from sensors, the web, and people is inherently probabilistic. Because programming languages use discrete types (floats, integers, and booleans), applications, ranging from GPS navigation to web search to polling, express and reason about uncertainty in idiosyncratic ways.

This mismatch causes three problems. (1) Using an estimate as a fact introduces errors (walking through [ ]Cited by: Chapter 9 Simple Linear Regression An analysis appropriate for a quantitative outcome and a single quantitative ex-planatory variable. The model behind linear regression When we are examining the relationship between a quantitative outcome and a single quantitative explanatory variable, simple linear regression is the most com-File Size: KB.

high additional costs for data transfer at runtime. Second, since the resulting scenario data is stored and accessed in an application-specific manner, it is not easily accessible from other applications. An approach to address those problems is to provide database support for rep-resenting and processing data in a scenario planning process.

robust optimization for data-driven newsvendor problems, in which the uncertainty set for the unknown distribution is defined as a “likelihood region”. Bertsimas and Brown [7] interpret robust optimization in terms of coherent risk measures. Ben-Tal et al. [6] consider the soft.

Estimation Techniques - Overview. Estimation is the process of finding an estimate, or approximation, which is a value that can be used for some purpose even if input data may be incomplete, uncertain, or unstable. Estimation determines how much money, effort, resources, and time it will take to build a specific system or product.

Not all that easily. Unfortunately, our knowledge about future conditions and events is inherently imperfect and incomplete. (As Yogi Berra famously said: “It’s tough to make predictions, especially about the future”). Uncertainty also arises abou. To try a problem scenario with your group, here is an easy to use instruction guide for writing your own.

Try it with your grade level or department team, too. And, by all means, eventually, have students develop their own problem scenarios around issues of interest or curricular topics from the scope and sequence of. examples of data that are uncertain.

We encounter different ways of dealing with uncertainty in literature. A popular tool is “sensitivity analysis” which is a post-optimality tool. It measures the sensitivity of a solution to changes in the input data.

Another way of dealing with uncertainty is File Size: KB. Addressing an Uncertain Future Using Scenario Analysis Afzal S Siddiqui and Chris Marnay Environmental Energy Technologies Division 15 December This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Planning, Analysis, and Evaluation section of Planning, Budget, and.

The sensitivity chart pdf allows you to determine the influence that each uncertain model input has individually on an output variable based on its correlation with the output variable. The sensitivity chart displays the rankings of each uncertain variable according to its impact on an output cell as .CHANCE-CONSTRAINED BINARY PACKING PROBLEMS YONGJIA SONG, Download pdf LUEDTKE AND S_IMGE K UC˘ UKYAVUZ Abstract.

We consider a class of packing problems with uncertain data, which we refer to as the chance-constrained binary packing problem. In this problem, a subset of items in contrast to our method for lifting probabilistic cover inequalities.

The conceivable worst-case scenario is of academic interest only; the plausible ebook possible worst-case scenarios are of greater relevance for policy and risk management. In the following three sections, applications of these ideas about worst-case scenarios are applied to emissions/concentrations, climate sensitivity and sea level rise.