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Abstract: Computational complexity has two goals: finding the inherent cost of some problem, and finding optimal algorithms for solving this problem. for which the available information is partial, contaminated by error, and priced. Examples of such problems include integration, approximation, ordinary and partial di#erential equations, integral equations, and the solution of nonlinear problems such as root-finding and optimization. In this talk, we give a brief overview of ibc. We focus mainly on the... (Update)
Active bibliography (related documents): More All
0.1: A Continuous Model of Computation - Traub (1998) (Correct)
0.1: Information-Based Complexity and Information-Based Optimization - Traub (1999) (Correct)
0.1: Complexity of Neural Network Approximation with Limited.. - Kon, Plaskota (2000) (Correct)
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0.3: What is the Complexity of Surface Integration? - Werschulz, Wozniakowski (2001) (Correct)
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BibTeX entry: (Update)
@misc{ werschulz-overview,
author = "Arthur Werschulz",
title = "An Overview of Information-Based Complexity",
url = "citeseer.ist.psu.edu/werschulz02overview.html" }
Citations (may not include all citations):134 Information-Based Complexity (context) - Traub, Wasilkowski et al. - 1988
42 Deterministic and Stochastic Error Bounds in Numerical Analy.. (context) - Novak - 1988
27 Complexity and Information (context) - Traub - 1998
12 Noisy Information and Computational Complexity (context) - Plaskota - 1996
1 Average-Case Analysis of Numerical Problems vol (context) - Ritter - 2000
Documents on the same site (http://www1.cs.columbia.edu/~agw/pubs.html):
Where Does Smoothness Count the Most for Fredholm Equations of.. - Werschulz (2002) (Correct)
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