George Forsythe and the Development of Computer Science by Donald E
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RESOURCES in NUMERICAL ANALYSIS Kendall E
RESOURCES IN NUMERICAL ANALYSIS Kendall E. Atkinson University of Iowa Introduction I. General Numerical Analysis A. Introductory Sources B. Advanced Introductory Texts with Broad Coverage C. Books With a Sampling of Introductory Topics D. Major Journals and Serial Publications 1. General Surveys 2. Leading journals with a general coverage in numerical analysis. 3. Other journals with a general coverage in numerical analysis. E. Other Printed Resources F. Online Resources II. Numerical Linear Algebra, Nonlinear Algebra, and Optimization A. Numerical Linear Algebra 1. General references 2. Eigenvalue problems 3. Iterative methods 4. Applications on parallel and vector computers 5. Over-determined linear systems. B. Numerical Solution of Nonlinear Systems 1. Single equations 2. Multivariate problems C. Optimization III. Approximation Theory A. Approximation of Functions 1. General references 2. Algorithms and software 3. Special topics 4. Multivariate approximation theory 5. Wavelets B. Interpolation Theory 1. Multivariable interpolation 2. Spline functions C. Numerical Integration and Differentiation 1. General references 2. Multivariate numerical integration IV. Solving Differential and Integral Equations A. Ordinary Differential Equations B. Partial Differential Equations C. Integral Equations V. Miscellaneous Important References VI. History of Numerical Analysis INTRODUCTION Numerical analysis is the area of mathematics and computer science that creates, analyzes, and implements algorithms for solving numerically the problems of continuous mathematics. Such problems originate generally from real-world applications of algebra, geometry, and calculus, and they involve variables that vary continuously; these problems occur throughout the natural sciences, social sciences, engineering, medicine, and business. During the second half of the twentieth century and continuing up to the present day, digital computers have grown in power and availability. -
You and Your Research & the Elements of Style
You and Your Research & The Elements of Style Philip Wadler University of Edinburgh Logic Mentoring Workshop Saarbrucken,¨ Monday 6 July 2020 Part I You and Your Research Richard W. Hamming, 1915–1998 • Los Alamos, 1945. • Bell Labs, 1946–1976. • Naval Postgraduate School, 1976–1998. • Turing Award, 1968. (Third time given.) • IEEE Hamming Medal, 1987. It’s not luck, it’s not brains, it’s courage Say to yourself, ‘Yes, I would like to do first-class work.’ Our society frowns on people who set out to do really good work. You’re not supposed to; luck is supposed to descend on you and you do great things by chance. Well, that’s a kind of dumb thing to say. ··· How about having lots of ‘brains?’ It sounds good. Most of you in this room probably have more than enough brains to do first-class work. But great work is something else than mere brains. ··· One of the characteristics of successful scientists is having courage. Once you get your courage up and believe that you can do important problems, then you can. If you think you can’t, almost surely you are not going to. — Richard Hamming, You and Your Research Develop reusable solutions How do I obey Newton’s rule? He said, ‘If I have seen further than others, it is because I’ve stood on the shoulders of giants.’ These days we stand on each other’s feet! Now if you are much of a mathematician you know that the effort to gen- eralize often means that the solution is simple. -
History of Modern Applied Mathematics in Mathematics Education
HISTORY OF MODERN APPLIED MATHEMATICS IN MATHEMATICS EDUCATION UFFE THOMAS JANKVISI [I] When conversations turn to using history of mathematics in in-issues, of mathematics. When using history as a tool to classrooms, the rnferent is typically the old, often antique, improve leaining or instruction, we may distinguish at least history of the discipline (e g, Calinger, 1996; Fauvel & van two different uses: history as a motivational or affective Maanen, 2000; Jahnke et al, 1996; Katz, 2000) [2] This tool, and histmy as a cognitive tool Together with history tendency might be expected, given that old mathematics is as a goal these two uses of histoty as a tool are used to struc often more closely related to school mathematics However, ture discussion of the educational benefits of choosing a there seem to be some clear advantages of including histo history of modern applied mathematics ries of more modern applied mathematics 01 histories of History as a goal 'in itself' does not refor to teaching his modem applications of mathematics [3] tory of mathematics per se, but using histo1y to surface One (justified) objection to integrating elements of the meta-aspects of the discipline Of course, in specific teach history of modetn applied mathematics is that it is often com ing situations, using histmy as a goal may have the positive plex and difficult While this may be so in most instances, it side effect of offering students insight into mathematical is worthwhile to search for cases where it isn't so I consider in-issues of a specific history But the impo1tant detail is three here. -
Efficient Space-Time Sampling with Pixel-Wise Coded Exposure For
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging Dengyu Liu, Jinwei Gu, Yasunobu Hitomi, Mohit Gupta, Tomoo Mitsunaga, Shree K. Nayar Abstract—Cameras face a fundamental tradeoff between spatial and temporal resolution. Digital still cameras can capture images with high spatial resolution, but most high-speed video cameras have relatively low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical hardware constraints on sampling schemes imposed by architectures of current image sensors, which means that our sampling function can be implemented on CMOS image sensors with modified control units in the future. We evaluate components of our approach - sampling function and sparse representation by comparing them to several existing approaches. We also implement a prototype imaging system with pixel-wise coded exposure control using a Liquid Crystal on Silicon (LCoS) device. System characteristics such as field of view, Modulation Transfer Function (MTF) are evaluated for our imaging system. Both simulations and experiments on a wide range of -
Applied Analysis & Scientific Computing Discrete Mathematics
CENTER FOR NONLINEAR ANALYSIS The CNA provides an environment to enhance and coordinate research and training in applied analysis, including partial differential equations, calculus of Applied Analysis & variations, numerical analysis and scientific computation. It advances research and educational opportunities at the broad interface between mathematics and Scientific Computing physical sciences and engineering. The CNA fosters networks and collaborations within CMU and with US and international institutions. Discrete Mathematics & Operations Research RANKINGS DOCTOR OF PHILOSOPHY IN ALGORITHMS, COMBINATORICS, U.S. News & World Report AND OPTIMIZATION #16 | Applied Mathematics Carnegie Mellon University offers an interdisciplinary Ph.D program in Algorithms, Combinatorics, and #7 | Discrete Mathematics and Combinatorics Optimization. This program is the first of its kind in the United States. It is administered jointly #6 | Best Graduate Schools for Logic by the Tepper School of Business (Operations Research group), the Computer Science Department (Algorithms and Complexity group), and the Quantnet Department of Mathematical Sciences (Discrete Mathematics group). #4 | Best Financial Engineering Programs Carnegie Mellon University does not CONTACT discriminate in admission, employment, or Logic administration of its programs or activities on Department of Mathematical Sciences the basis of race, color, national origin, sex, handicap or disability, age, sexual orientation, 5000 Forbes Avenue gender identity, religion, creed, ancestry, -
Metal Complexes of Penicillin and Cephalosporin Antibiotics
I METAL COMPLEXES OF PENICILLIN AND CEPHALOSPORIN ANTIBIOTICS A thesis submitted to THE UNIVERSITY OF CAPE TOWN in fulfilment of the requirement$ forTown the degree of DOCTOR OF PHILOSOPHY Cape of by GRAHAM E. JACKSON University Department of Chernis try, University of Cape Town, Rondebosch, Cape, · South Africa. September 1975. The copyright of th:s the~is is held by the University of C::i~r:: To\vn. Reproduction of i .. c whole or any part \ . may be made for study purposes only, and \; not for publication. The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgementTown of the source. The thesis is to be used for private study or non- commercial research purposes only. Cape Published by the University ofof Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University '· ii ACKNOWLEDGEMENTS I would like to express my sincere thanks to my supervisors: Dr. L.R. Nassimbeni, Dr. P.W. Linder and Dr. G.V. Fazakerley for their invaluable guidance and friendship throughout the course of this work. I would also like to thank my colleagues, Jill Russel, Melanie Wolf and Graham Mortimor for their many useful conrrnents. I am indebted to AE & CI for financial assistance during the course of. this study. iii ABSTRACT The interaction between metal"'.'ions and the penici l)in and cephalosporin antibiotics have been studied in an attempt to determine both the site and mechanism of this interaction. The solution conformation of the Cu(II) and Mn(II) complexes were determined using an n.m.r, line broadening, technique. -
Computing As Engineering
Journal of Universal Computer Science, vol. 15, no. 8 (2009), 1642-1658 submitted: 14/3/09, accepted: 27/4/09, appeared: 28/4/09 © J.UCS Computing as Engineering Matti Tedre (Tumaini University, Iringa, Tanzania fi[email protected]) Abstract: Computing as a discipline is often characterized as a combination of three major traditions: theoretical, scientific, and engineering tradition. Although the three traditions are all considered equally necessary for modern computing, the engineering tradition is often considered to be useful but to lack intellectual depth. This article discusses the basic intellectual background of the engineering tradition of computing. The article depicts the engineering aims manifest in the academic field of computing, compares the engineering tradition with the other traditions of computing as a disci- pline, and presents some epistemological, ontological, and methodological views con- cerning the engineering tradition of computing. The article aims at giving the reader an overview of the engineering tradition in computing and of some open questions about the intellectual foundations and contributions of the engineering tradition in computing. Key Words: information technology, philosophy of computer science, philosophy of technology, computing, engineering Category: K.7, K.7.1, K.7.m 1 Introduction The juxtaposing of science and technology is perhaps nowhere else as marked as in the computing disciplines. The division of computing into its mathemat- ical/theoretical, scientific/empirical, and design/engineering traditions ([Weg- ner, 1976], [Denning et al., 1989]) has spurred fiery debates about the merits and shortcomings of each tradition. In those debates, the theoretical tradition leans on the recognition of mathematics and logic as the theoretical cornerstones of computing, the scientific tradition draws support from arguments from the philosophy of science, but the design/engineering tradition is usually only rec- ognized for its utility and not for its intellectual foundations. -
Quality Improvement of Compressed Color Images Using a Probabilistic Approach
QUALITY IMPROVEMENT OF COMPRESSED COLOR IMAGES USING A PROBABILISTIC APPROACH Nobuteru Takao, Shun Haraguchi, Hideki Noda, Michiharu Niimi Kyushu Institute of Technology, Dept. of Systems Design and Informatics, 680-4 Kawazu, Iizuka, 820-8502 Japan E-mail: {takao, haraguchi, noda, niimi}@mip.ces.kyutech.ac.jp ABSTRACT method using information on luminance component which is not downsampled. In compressed color images, colors are usually represented by luminance and chrominance (YCbCr) components. Con- The interpolation aims to recover only resolution of chromi- sidering characteristics of human vision system, chromi- nance components lost by downsampling. Alternatively, we nance (CbCr) components are generally represented more aim to recover not only resolution lost by downsampling coarsely than luminance component. Aiming at possible re- but also precision lost by a coarser quantization, if possi- covery of chrominance components, we propose a model- ble. Aiming at such recovery of chrominance components, based chrominance estimation algorithm where color im- we propose a model-based method where color images are ages are modeled by a Markov random field (MRF). A sim- modeled by a Markov random field (MRF). A simple MRF ple MRF model is here used whose local conditional proba- model is here used whose local conditional probability den- bility density function (pdf) for a color vector of a pixel is a sity function (pdf) for a color vector of a pixel is a Gaussian Gaussian pdf depending on color vectors of its neighboring pdf depending on color vectors of its neighboring pixels. pixels. Chrominance components of a pixel are estimated Chrominance components of a pixel are estimated by max- by maximizing the conditional pdf given its luminance com- imizing the conditional pdf given its luminance component ponent and its neighboring color vectors. -
Numerical Analysis to Quantum Computing
Credit: evv/Shutterstock.com and D-Wave, Inc. and D-Wave, Credit: evv/Shutterstock.com How NASA-USRA collaborations have advanced knowledge in and with the use of new computing technologies. When USRA was created in 1969, later its first task was the management the Chief Scientist at the FROM of the Lunar Science Institute near Center. In February of 1972, Duberg NASA’s Manned Spacecraft Center wrote a memorandum to the senior NUMERICAL (now the Johnson Space Center). management of LaRC, expressing A little more than three years his view that: TO later, USRA began to manage the ANALYSIS Institute for Computer Applications The field of computers and their in Science and Engineering (ICASE) application in the scientific QUANTUM at NASA’s Langley Research Center community has had a profound (LaRC). The rationale for creating effect on the progress of ICASE was developed by Dr. John aerospace research as well as COMPUTING E. Duberg (1917-2002), who was technology in general for the the Associate Director at LaRC and past 15 years. With the advent of “super computers,” based on parallel and pipeline techniques, the potentials for research and problem solving in the future seem even more promising and challenging. The only question is how long will it take to identify the potentials, harness the power, and develop the disciplines necessary to employ such tools effectively.1 Twenty years later, Duberg reflected on the creation of ICASE: By the 1970s, Langley’s computing capabilities had kept pace with the rapidly developing John E. Duberg, Chief Scientist, LaRC; George M. -
OF the AMERICAN MATHEMATICAL SOCIETY 157 Notices February 2019 of the American Mathematical Society
ISSN 0002-9920 (print) ISSN 1088-9477 (online) Notices ofof the American MathematicalMathematical Society February 2019 Volume 66, Number 2 THE NEXT INTRODUCING GENERATION FUND Photo by Steve Schneider/JMM Steve Photo by The Next Generation Fund is a new endowment at the AMS that exclusively supports programs for doctoral and postdoctoral scholars. It will assist rising mathematicians each year at modest but impactful levels, with funding for travel grants, collaboration support, mentoring, and more. Want to learn more? Visit www.ams.org/nextgen THANK YOU AMS Development Offi ce 401.455.4111 [email protected] A WORD FROM... Robin Wilson, Notices Associate Editor In this issue of the Notices, we reflect on the sacrifices and accomplishments made by generations of African Americans to the mathematical sciences. This year marks the 100th birthday of David Blackwell, who was born in Illinois in 1919 and went on to become the first Black professor at the University of California at Berkeley and one of America’s greatest statisticians. Six years after Blackwell was born, in 1925, Frank Elbert Cox was to become the first Black mathematician when he earned his PhD from Cornell University, and eighteen years later, in 1943, Euphemia Lofton Haynes would become the first Black woman to earn a mathematics PhD. By the late 1960s, there were close to 70 Black men and women with PhDs in mathematics. However, this first generation of Black mathematicians was forced to overcome many obstacles. As a Black researcher in America, segregation in the South and de facto segregation elsewhere provided little access to research universities and made it difficult to even participate in professional societies. -
Mathematics (MATH) 1
Mathematics (MATH) 1 MATHEMATICS (MATH) MATH 505: Mathematical Fluid Mechanics 3 Credits MATH 501: Real Analysis Kinematics, balance laws, constitutive equations; ideal fluids, viscous 3 Credits flows, boundary layers, lubrication; gas dynamics. Legesgue measure theory. Measurable sets and measurable functions. Prerequisite: MATH 402 or MATH 404 Legesgue integration, convergence theorems. Lp spaces. Decomposition MATH 506: Ergodic Theory and differentiation of measures. Convolutions. The Fourier transform. MATH 501 Real Analysis I (3) This course develops Lebesgue measure 3 Credits and integration theory. This is a centerpiece of modern analysis, providing a key tool in many areas of pure and applied mathematics. The course Measure-preserving transformations and flows, ergodic theorems, covers the following topics: Lebesgue measure theory, measurable sets ergodicity, mixing, weak mixing, spectral invariants, measurable and measurable functions, Lebesgue integration, convergence theorems, partitions, entropy, ornstein isomorphism theory. Lp spaces, decomposition and differentiation of measures, convolutions, the Fourier transform. Prerequisite: MATH 502 Prerequisite: MATH 404 MATH 507: Dynamical Systems I MATH 502: Complex Analysis 3 Credits 3 Credits Fundamental concepts; extensive survey of examples; equivalence and classification of dynamical systems, principal classes of asymptotic Complex numbers. Holomorphic functions. Cauchy's theorem. invariants, circle maps. Meromorphic functions. Laurent expansions, residue calculus. Conformal -
GEORGE ELMER FORSYTHE January 8, 1917—April 9, 1972
MATHEMATICS OF COMPUTATION, VOLUME 26, NUMBER 120, OCTOBER 1972 GEORGE ELMER FORSYTHE January 8, 1917—April9, 1972 With the sudden death of George Forsythe, modern numerical analysis has lost one of its earliest pioneers, an influential leader who helped bring numerical analysis and computer science to their present positions. We in computing have been fortunate to have benefited from George's abilities, and we all feel a deep sense of personal and professional loss. Although George's interest in calculation can be traced back at least to his seventh grade attempt to see how the digits repeated in the decimal expansion of 10000/7699, his early research was actually in summability theory, leading to his Ph.D. in 1941 under J. D. Tamarkin at Brown University. A brief period at Stanford thereafter was interrupted by his Air Force service, during which he became interested in meteor- ology, co-authoring an influential book in that field. George then spent a year at Boeing, where his interest in scientific computing was reawakened, and in 1948 he began his fruitful association with the Institute for Numerical Analysis of the National Bureau of Standards on the UCLA campus, remaining at UCLA for three years after leaving the Institute in 1954. In 1957 he rejoined the Mathematics Department at Stanford as Professor, became director of the Computation Center, and eventually Chairman of the Computer Science Department when it was founded in 1965. George greatly influenced the development of modern numerical analysis, especially in numerical linear algebra where his 1953 classification of methods for solving linear systems gave structure to a field of great importance.