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Special Western Canada Linear Algebra Meeting (17W2668)
Special Western Canada Linear Algebra Meeting (17w2668) Hadi Kharaghani (University of Lethbridge), Shaun Fallat (University of Regina), Pauline van den Driessche (University of Victoria) July 7, 2017 – July 9, 2017 1 History of this Conference Series The Western Canada Linear Algebra meeting, or WCLAM, is a regular series of meetings held roughly once every 2 years since 1993. Professor Peter Lancaster from the University of Calgary initiated the conference series and has been involved as a mentor, an organizer, a speaker and participant in all of the ten or more WCLAM meetings. Two broad goals of the WCLAM series are a) to keep the community abreast of current research in this discipline, and b) to bring together well-established and early-career researchers to talk about their work in a relaxed academic setting. This particular conference brought together researchers working in a wide-range of mathematical fields including matrix analysis, combinatorial matrix theory, applied and numerical linear algebra, combinatorics, operator theory and operator algebras. It attracted participants from most of the PIMS Universities, as well as Ontario, Quebec, Indiana, Iowa, Minnesota, North Dakota, Virginia, Iran, Japan, the Netherlands and Spain. The highlight of this meeting was the recognition of the outstanding and numerous contributions to the areas of research of Professor Peter Lancaster. Peter’s work in mathematics is legendary and in addition his footprint on mathematics in Canada is very significant. 2 Presentation Highlights The conference began with a presentation from Prof. Chi-Kwong Li surveying the numerical range and connections to dilation. During this lecture the following conjecture was stated: If A 2 M3 has no reducing eigenvalues, then there is a T 2 B(H) such that the numerical range of T is contained in that of A, and T has no dilation of the form I ⊗ A. -
500 Natural Sciences and Mathematics
500 500 Natural sciences and mathematics Natural sciences: sciences that deal with matter and energy, or with objects and processes observable in nature Class here interdisciplinary works on natural and applied sciences Class natural history in 508. Class scientific principles of a subject with the subject, plus notation 01 from Table 1, e.g., scientific principles of photography 770.1 For government policy on science, see 338.9; for applied sciences, see 600 See Manual at 231.7 vs. 213, 500, 576.8; also at 338.9 vs. 352.7, 500; also at 500 vs. 001 SUMMARY 500.2–.8 [Physical sciences, space sciences, groups of people] 501–509 Standard subdivisions and natural history 510 Mathematics 520 Astronomy and allied sciences 530 Physics 540 Chemistry and allied sciences 550 Earth sciences 560 Paleontology 570 Biology 580 Plants 590 Animals .2 Physical sciences For astronomy and allied sciences, see 520; for physics, see 530; for chemistry and allied sciences, see 540; for earth sciences, see 550 .5 Space sciences For astronomy, see 520; for earth sciences in other worlds, see 550. For space sciences aspects of a specific subject, see the subject, plus notation 091 from Table 1, e.g., chemical reactions in space 541.390919 See Manual at 520 vs. 500.5, 523.1, 530.1, 919.9 .8 Groups of people Add to base number 500.8 the numbers following —08 in notation 081–089 from Table 1, e.g., women in science 500.82 501 Philosophy and theory Class scientific method as a general research technique in 001.4; class scientific method applied in the natural sciences in 507.2 502 Miscellany 577 502 Dewey Decimal Classification 502 .8 Auxiliary techniques and procedures; apparatus, equipment, materials Including microscopy; microscopes; interdisciplinary works on microscopy Class stereology with compound microscopes, stereology with electron microscopes in 502; class interdisciplinary works on photomicrography in 778.3 For manufacture of microscopes, see 681. -
Mathematics (MATH) 1
Mathematics (MATH) 1 MATH 103 College Algebra and Trigonometry MATHEMATICS (MATH) Prerequisites: Appropriate score on the Math Placement Exam; or grade of P, C, or better in MATH 100A. MATH 100A Intermediate Algebra Notes: Credit for both MATH 101 and 103 is not allowed; credit for both Prerequisites: Appropriate score on the Math Placement Exam. MATH 102 and MATH 103 is not allowed; students with previous credit in Notes: Credit earned in MATH 100A will not count toward degree any calculus course (Math 104, 106, 107, or 208) may not earn credit for requirements. this course. Description: Review of the topics in a second-year high school algebra Description: First and second degree equations and inequalities, absolute course taught at the college level. Includes: real numbers, 1st and value, functions, polynomial and rational functions, exponential and 2nd degree equations and inequalities, linear systems, polynomials logarithmic functions, trigonometric functions and identities, laws of and rational expressions, exponents and radicals. Heavy emphasis on sines and cosines, applications, polar coordinates, systems of equations, problem solving strategies and techniques. graphing, conic sections. Credit Hours: 3 Credit Hours: 5 Max credits per semester: 3 Max credits per semester: 5 Max credits per degree: 3 Max credits per degree: 5 Grading Option: Graded with Option Grading Option: Graded with Option Prerequisite for: MATH 100A; MATH 101; MATH 103 Prerequisite for: AGRO 361, GEOL 361, NRES 361, SOIL 361, WATS 361; MATH 101 College Algebra AGRO 458, AGRO 858, NRES 458, NRES 858, SOIL 458; ASCI 340; Prerequisites: Appropriate score on the Math Placement Exam; or grade CHEM 105A; CHEM 109A; CHEM 113A; CHME 204; CRIM 300; of P, C, or better in MATH 100A. -
Book2018.Pdf
Welcome Welcome to Santiago 2018! We are delighted to welcome friends and colleagues to our home city of Santiago for the XII GAFEVOL Congress 2018. Continuing the successful trend of past Congresses, the 2018 Congress in Santiago shows how important the GAFEVOL Congress has become as a regular platform accommodating the rapid pace of progress in the Evolution Equations and Functional Analysis field and for presenting the results of studies which have a direct impact on the applications. GAFEVOL 2018 will provide a Scientific Programme that builds on the highly successful models from previous Congresses, while incorporating innovative suggestions from valuable stakeholders. This Congress will feature presentations of some of the most recent research including controllability of systems, chaotic dynamics of non local models, advances on existence, periodicity, mild solutions, classical solutions, extensions, global bifurcation, and convergence of solutions for different classes of ODE's and PDE's. Also, applications in stochastic models in epidemiology and damage models will be treated. We wish you a pleasant stay in Santiago. Enjoy the Congress!. XII Congress GAFEVOL 4 XII Congress GAFEVOL Organizing Committee Carlos Lizama (U. de Santiago) Hern´anHenr´ıquez(U. de Santiago) Sebasti´anZamorano (U. Santiago) Edgardo Alvarez´ (U. del Norte) Rodrigo Ponce (U. de Talca) Scientific Committee Manuel Pinto (U. de Chile) Carlos Conca (U. de Chile) Jaqueline Mesquita (U. de Bras´ılia) Mahamadi Warma (U. de Puerto Rico) Rolando Rebolledo (U. de Valpara´ıso) -
Newsletter · Wednesday 17Th
KEEP UPDATED NEWSLETTER · WEDNESDAY 17TH INDUSTRY DAY The Industry Day is a mathematical technology transfer oriented activity that will take place for the first time in an ICIAM event. Its main goals will be to show to the industrial sector the high innovation potential of the mathematical tools of Statistics & Big Data and MSO (Modelling, Simulation and Optimization) and also to open new channels of communication between researchers and potential users of mathematical tools. Room: FE-1A (FE Building, Aula Magna, 1st floor) Here is the program schedule: 10:30-11:00 WELCOME SESSION With María Vicenta Mestre, Rector of the University of Valencia, Javier Ponce, Director of the Center for the Industry Technological Development, Maria Esteban, President of ICIAM and Peregrina Quintela, President of Spanish Mathematics-Industry (Math-in) Network. 11:00-13:00 FIRST SESSION Mathematics a key enabler for Digital Twins, by Dirk Hartmann. Siemens, Corporate Technology (Germany) The Ferrosolar project: industrial R&D&I for new applications of silicon by Ramón Ordás Badía. Chief Executive Officer of Silicio Ferrosolar (Spain) Methods and issues for the analysis of complex systems by numerical simulation at EDF by Bertrand Iooss. Senior researcher in EDF R&D, Department PRISME, Chatou (France). To BEM, or not to BEM, that is the question by Stefan Kurz. Vice President for Strategies and Technologies of Modeling and Simulation, Corporate Sector Research and Advance Engineering, Robert Bosch GmbH, (Germany). 13:00-14:30 LUNCH BREAK 14:30-16:30 SECOND SESSION How to embed artificial intelligence in a Bank? by Julien Crowe. Leader, Artificial Intelligence. -
Poster Sessions
Second WHO Global Forum on Medical Devices Posters A. Economics of medical devices Atrial septal defect (ASD) device closure using a device sizing formula without balloon sizing or invasive A.01 echocardiography is safe, cost effective and increases access to treatment Gamini Galappatthy, Ruvan Ekanayaka, The National Hospital of Sri Lanka, Sri Lanka A.02 International regulatory convergence: meeting patients needs Jo Groves, International Alliance of Patient's Organizations A.03 Medical device procurement strategies at a glance in Turkish public hospital organization Ali Riza Demirbas, Ministry of Health, Turkey; Fetin Rüstü Yildiz, Zehra Yaman, Turkish Public Hospital Organization, Turkey A.04 Computerization and rationalization of the investment in medical technologies in hospitals Lisa Giuliani, Fugieri Stefania, Elettronica Bio Medicale, TBS Group, Italy; Tulli Giorgio, Azienda Sanitaria Locale Nr 10, Italy A.05 Cost effectiveness of medical devices to reduce mortality from pre-eclampsia in low-resource settings Zoë M McLaren, John P Hessburg, Amir Sabet Sarvestani, Ethan Parker, Timothy R B Johnson, Kathleen H Sienko, University of Michigan, United States of America; James Akazili, Navrongo Health Research Centre, Ghana A.06 Comparison of reimbursement systems: Turkey, Germany and United Kingdom Gorkey Turgut, Turkey; Funda Özdiler, Ismet Köksal, Osman Nacar, Ercan Simsek, Turkish Medicine and Medical Devices Agency, Turkey A.07 Standarisation des equipements biomedicaux des laboratories Dr. Tra Bi Yrié-Denis, Ministére de la Santé -
"Mathematics in Science, Social Sciences and Engineering"
Research Project "Mathematics in Science, Social Sciences and Engineering" Reference person: Prof. Mirko Degli Esposti (email: [email protected]) The Department of Mathematics of the University of Bologna invites applications for a PostDoc position in "Mathematics in Science, Social Sciences and Engineering". The appointment is for one year, renewable to a second year. Candidates are expected to propose and perform research on innovative aspects in mathematics, along one of the following themes: Analysis: 1. Functional analysis and abstract equations: Functional analytic methods for PDE problems; degenerate or singular evolution problems in Banach spaces; multivalued operators in Banach spaces; function spaces related to operator theory.FK percolation and its applications 2. Applied PDEs: FK percolation and its applications, mathematical modeling of financial markets by PDE or stochastic methods; mathematical modeling of the visual cortex in Lie groups through geometric analysis tools. 3. Evolutions equations: evolutions equations with real characteristics, asymptotic behavior of hyperbolic systems. 4. Qualitative theory of PDEs and calculus of variations: Sub-Riemannian PDEs; a priori estimates and solvability of systems of linear PDEs; geometric fully nonlinear PDEs; potential analysis of second order PDEs; hypoellipticity for sums of squares; geometric measure theory in Carnot groups. Numerical Analysis: 1. Numerical Linear Algebra: matrix equations, matrix functions, large-scale eigenvalue problems, spectral perturbation analysis, preconditioning techniques, ill- conditioned linear systems, optimization problems. 2. Inverse Problems and Image Processing: regularization and optimization methods for ill-posed integral equation problems, image segmentation, deblurring, denoising and reconstruction from projection; analysis of noise models, medical applications. 3. Geometric Modelling and Computer Graphics: Curves and surface modelling, shape basic functions, interpolation methods, parallel graphics processing, realistic rendering. -
Randnla: Randomized Numerical Linear Algebra
review articles DOI:10.1145/2842602 generation mechanisms—as an algo- Randomization offers new benefits rithmic or computational resource for the develop ment of improved algo- for large-scale linear algebra computations. rithms for fundamental matrix prob- lems such as matrix multiplication, BY PETROS DRINEAS AND MICHAEL W. MAHONEY least-squares (LS) approximation, low- rank matrix approxi mation, and Lapla- cian-based linear equ ation solvers. Randomized Numerical Linear Algebra (RandNLA) is an interdisci- RandNLA: plinary research area that exploits randomization as a computational resource to develop improved algo- rithms for large-scale linear algebra Randomized problems.32 From a foundational per- spective, RandNLA has its roots in theoretical computer science (TCS), with deep connections to mathemat- Numerical ics (convex analysis, probability theory, metric embedding theory) and applied mathematics (scientific computing, signal processing, numerical linear Linear algebra). From an applied perspec- tive, RandNLA is a vital new tool for machine learning, statistics, and data analysis. Well-engineered implemen- Algebra tations have already outperformed highly optimized software libraries for ubiquitous problems such as least- squares,4,35 with good scalability in par- allel and distributed environments. 52 Moreover, RandNLA promises a sound algorithmic and statistical foundation for modern large-scale data analysis. MATRICES ARE UBIQUITOUS in computer science, statistics, and applied mathematics. An m × n key insights matrix can encode information about m objects ˽ Randomization isn’t just used to model noise in data; it can be a powerful (each described by n features), or the behavior of a computational resource to develop discretized differential operator on a finite element algorithms with improved running times and stability properties as well as mesh; an n × n positive-definite matrix can encode algorithms that are more interpretable in the correlations between all pairs of n objects, or the downstream data science applications. -
Numerical Linear Algebra and Matrix Analysis
Numerical Linear Algebra and Matrix Analysis Higham, Nicholas J. 2015 MIMS EPrint: 2015.103 Manchester Institute for Mathematical Sciences School of Mathematics The University of Manchester Reports available from: http://eprints.maths.manchester.ac.uk/ And by contacting: The MIMS Secretary School of Mathematics The University of Manchester Manchester, M13 9PL, UK ISSN 1749-9097 1 exploitation of matrix structure (such as sparsity, sym- Numerical Linear Algebra and metry, and definiteness), and the design of algorithms y Matrix Analysis to exploit evolving computer architectures. Nicholas J. Higham Throughout the article, uppercase letters are used for matrices and lower case letters for vectors and scalars. Matrices are ubiquitous in applied mathematics. Matrices and vectors are assumed to be complex, unless ∗ Ordinary differential equations (ODEs) and partial dif- otherwise stated, and A = (aji) denotes the conjugate ferential equations (PDEs) are solved numerically by transpose of A = (aij ). An unsubscripted norm k · k finite difference or finite element methods, which lead denotes a general vector norm and the corresponding to systems of linear equations or matrix eigenvalue subordinate matrix norm. Particular norms used here problems. Nonlinear equations and optimization prob- are the 2-norm k · k2 and the Frobenius norm k · kF . lems are typically solved using linear or quadratic The notation “i = 1: n” means that the integer variable models, which again lead to linear systems. i takes on the values 1; 2; : : : ; n. Solving linear systems of equations is an ancient task, undertaken by the Chinese around 1AD, but the study 1 Nonsingularity and Conditioning of matrices per se is relatively recent, originating with Arthur Cayley’s 1858 “A Memoir on the Theory of Matri- Nonsingularity of a matrix is a key requirement in many ces”. -
A History of Modern Numerical Linear Algebra
Gene Golub, Stanford University 1 A History of Modern Numerical Linear Algebra Gene H. Golub Stanford University Gene Golub, Stanford University 2 What is Numerical Analysis? (a.k.a Scientific Computing) The definitions are becoming shorter... Webster’s New Collegiate Dictionary (1973): • ”The study of quantitative approximations to the solutions of mathematical problems including consideration of the errors and bounds to the errors involved.” The American Heritage Dictionary (1992): • ”The study of approximate solutions to mathematical problems, taking into account the extent of possible errors.” L. N. Trefethen, Oxford University (1992): • ”The study of algorithms for the problems of continuous mathematics.” Gene Golub, Stanford University 3 Numerical Analysis: How It All Started Numerical analysis motivated the development of the earliest computers. Ballistics • Solution of PDE’s • Data Analysis • Early pioneers included: J. von Neumann, A. M. Turing In the beginning... von Neumann & Goldstine (1947): ”Numerical Inversion of Matrices of High Order” Gene Golub, Stanford University 4 Numerical Linear Algebra Numerical Linear Algebra (NLA) is a small but active area of research: Less than 200 active, committed persons. But the community involves many scientists. Gene Golub, Stanford University 5 Top Ten Algorithms in Science (Jack Dongarra, 2000) 1. Metropolis Algorithm for Monte Carlo 2. Simplex Method for Linear Programming 3. Krylov Subspace Iteration Methods 4. The Decompositional Approach to Matrix Computations 5. The Fortran Optimizing Compiler 6. QR Algorithm for Computing Eigenvalues 7. Quicksort Algorithm for Sorting 8. Fast Fourier Transform 9. Integer Relation Detection 10. Fast Multipole Method Red: Algorithms within the exclusive domain of NLA research. • Blue: Algorithms strongly (though not exclusively) connected to NLA research. -
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 -
Boletın De La Sociedad Espa˜Nola De Matemática Aplicada Sema Grupo
Bolet¶³n de la Sociedad Espanola~ de Matem¶atica Aplicada S~eMA Grupo Editor J.J. Vald¶es Garc¶³a (U. de Oviedo) E. Fern¶andez Cara (U. de Sevilla) B. Dugnol Alv¶ arez (U. de Oviedo) M. Mateos Alberdi (U. de Oviedo) C.O. Men¶endez P¶erez (U. de Oviedo) P. P¶erez Riera (U. de Oviedo) Comit¶e Cient¶³¯co E. Fern¶andez Cara (U. de Sevilla) A. Bermudez¶ de Castro (U. de Santiago) E. Casas Renter¶³a (U. de Cantabria) J.L. Cruz Soto (U. de C¶ordoba) J.M. Maz¶on Ruiz (U. de Valencia) I. Peral Alonso (U. Aut. de Madrid) J.J. Vald¶es Garc¶³a (U. de Oviedo) J.L. V¶azquez Su¶arez (U. Aut. de Madrid) L. Vega Gonz¶alez (U. del Pa¶³s Vasco) E. Zuazua Iriondo (U. Comp. de Madrid) Responsables de secciones Art¶³culos: E. Fern¶andez Cara (U. de Sevilla) Resumenes¶ de libros: F.J. Sayas Gonz¶alez (U. de Zaragoza) Noticias de S~eMA: R. Pardo San Gil (Secretaria de S~eMA) Congresos y Seminarios: J. Maz¶on Ruiz (U. de Valencia) Matem¶aticas e Industria: M. Lezaun Iturralde (U. del Pa¶³s Vasco) Educaci¶on Matem¶atica R. Rodr¶³guez del R¶³o (U. Comp. de Madrid) P¶agina web de S~eMA http://www.uca.es/sema/ Direcci¶on Editorial: Bolet¶³n de S~eMA. Dpto. de Matem¶aticas. Universidad de Oviedo. Avda. de Calvo Sotelo, s/n. 33007-Oviedo. [email protected] ISSN 1575-9822 Dep¶osito Legal: AS-1442-2002 Imprime: Grupo Bit¶acora.