Letter from the SIGSAM Chair
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Using Macaulay2 Effectively in Practice
Using Macaulay2 effectively in practice Mike Stillman ([email protected]) Department of Mathematics Cornell 22 July 2019 / IMA Sage/M2 Macaulay2: at a glance Project started in 1993, Dan Grayson and Mike Stillman. Open source. Key computations: Gr¨obnerbases, free resolutions, Hilbert functions and applications of these. Rings, Modules and Chain Complexes are first class objects. Language which is comfortable for mathematicians, yet powerful, expressive, and fun to program in. Now a community project Journal of Software for Algebra and Geometry (started in 2009. Now we handle: Macaulay2, Singular, Gap, Cocoa) (original editors: Greg Smith, Amelia Taylor). Strong community: including about 2 workshops per year. User contributed packages (about 200 so far). Each has doc and tests, is tested every night, and is distributed with M2. Lots of activity Over 2000 math papers refer to Macaulay2. History: 1976-1978 (My undergrad years at Urbana) E. Graham Evans: asked me to write a program to compute syzygies, from Hilbert's algorithm from 1890. Really didn't work on computers of the day (probably might still be an issue!). Instead: Did computation degree by degree, no finishing condition. Used Buchsbaum-Eisenbud \What makes a complex exact" (by hand!) to see if the resulting complex was exact. Winfried Bruns was there too. Very exciting time. History: 1978-1983 (My grad years, with Dave Bayer, at Harvard) History: 1978-1983 (My grad years, with Dave Bayer, at Harvard) I tried to do \real mathematics" but Dave Bayer (basically) rediscovered Groebner bases, and saw that they gave an algorithm for computing all syzygies. I got excited, dropped what I was doing, and we programmed (in Pascal), in less than one week, the first version of what would be Macaulay. -
Plotting Direction Fields in Matlab and Maxima – a Short Tutorial
Plotting direction fields in Matlab and Maxima – a short tutorial Luis Carvalho Introduction A first order differential equation can be expressed as dx x0(t) = = f(t; x) (1) dt where t is the independent variable and x is the dependent variable (on t). Note that the equation above is in normal form. The difficulty in solving equation (1) depends clearly on f(t; x). One way to gain geometric insight on how the solution might look like is to observe that any solution to (1) should be such that the slope at any point P (t0; x0) is f(t0; x0), since this is the value of the derivative at P . With this motivation in mind, if you select enough points and plot the slopes in each of these points, you will obtain a direction field for ODE (1). However, it is not easy to plot such a direction field for any function f(t; x), and so we must resort to some computational help. There are many computational packages to help us in this task. This is a short tutorial on how to plot direction fields for first order ODE’s in Matlab and Maxima. Matlab Matlab is a powerful “computing environment that combines numeric computation, advanced graphics and visualization” 1. A nice package for plotting direction field in Matlab (although resourceful, Matlab does not provide such facility natively) can be found at http://math.rice.edu/»dfield, contributed by John C. Polking from the Dept. of Mathematics at Rice University. To install this add-on, simply browse to the files for your version of Matlab and download them. -
A Note on Integral Points on Elliptic Curves 3
Journal de Th´eorie des Nombres de Bordeaux 00 (XXXX), 000–000 A note on integral points on elliptic curves par Mark WATKINS Abstract. We investigate a problem considered by Zagier and Elkies, of finding large integral points on elliptic curves. By writ- ing down a generic polynomial solution and equating coefficients, we are led to suspect four extremal cases that still might have non- degenerate solutions. Each of these cases gives rise to a polynomial system of equations, the first being solved by Elkies in 1988 using the resultant methods of Macsyma, with there being a unique rational nondegenerate solution. For the second case we found that resultants and/or Gr¨obner bases were not very efficacious. Instead, at the suggestion of Elkies, we used multidimensional p-adic Newton iteration, and were able to find a nondegenerate solution, albeit over a quartic number field. Due to our methodol- ogy, we do not have much hope of proving that there are no other solutions. For the third case we found a solution in a nonic number field, but we were unable to make much progress with the fourth case. We make a few concluding comments and include an ap- pendix from Elkies regarding his calculations and correspondence with Zagier. Resum´ e.´ A` la suite de Zagier et Elkies, nous recherchons de grands points entiers sur des courbes elliptiques. En ´ecrivant une solution polynomiale g´en´erique et en ´egalisant des coefficients, nous obtenons quatre cas extr´emaux susceptibles d’avoir des so- lutions non d´eg´en´er´ees. Chacun de ces cas conduit `aun syst`eme d’´equations polynomiales, le premier ´etant r´esolu par Elkies en 1988 en utilisant les r´esultants de Macsyma; il admet une unique solution rationnelle non d´eg´en´er´ee. -
Performance Analysis of Effective Symbolic Methods for Solving Band Matrix Slaes
Performance Analysis of Effective Symbolic Methods for Solving Band Matrix SLAEs Milena Veneva ∗1 and Alexander Ayriyan y1 1Joint Institute for Nuclear Research, Laboratory of Information Technologies, Joliot-Curie 6, 141980 Dubna, Moscow region, Russia Abstract This paper presents an experimental performance study of implementations of three symbolic algorithms for solving band matrix systems of linear algebraic equations with heptadiagonal, pen- tadiagonal, and tridiagonal coefficient matrices. The only assumption on the coefficient matrix in order for the algorithms to be stable is nonsingularity. These algorithms are implemented using the GiNaC library of C++ and the SymPy library of Python, considering five different data storing classes. Performance analysis of the implementations is done using the high-performance computing (HPC) platforms “HybriLIT” and “Avitohol”. The experimental setup and the results from the conducted computations on the individual computer systems are presented and discussed. An analysis of the three algorithms is performed. 1 Introduction Systems of linear algebraic equations (SLAEs) with heptadiagonal (HD), pentadiagonal (PD) and tridiagonal (TD) coefficient matrices may arise after many different scientific and engineering prob- lems, as well as problems of the computational linear algebra where finding the solution of a SLAE is considered to be one of the most important problems. On the other hand, special matrix’s char- acteristics like diagonal dominance, positive definiteness, etc. are not always feasible. The latter two points explain why there is a need of methods for solving of SLAEs which take into account the band structure of the matrices and do not have any other special requirements to them. One possible approach to this problem is the symbolic algorithms. -
Luis David Garcıa Puente
Luis David Garc´ıa Puente Department of Mathematics and Statistics (936) 294-1581 Sam Houston State University [email protected] Huntsville, TX 77341–2206 http://www.shsu.edu/ldg005/ Professional Preparation Universidad Nacional Autonoma´ de Mexico´ (UNAM) Mexico City, Mexico´ B.S. Mathematics (with Honors) 1999 Virginia Polytechnic Institute and State University Blacksburg, VA Ph.D. Mathematics 2004 – Advisor: Reinhard Laubenbacher – Dissertation: Algebraic Geometry of Bayesian Networks University of California, Berkeley Berkeley, CA Postdoctoral Fellow Summer 2004 – Mentor: Lior Pachter Mathematical Sciences Research Institute (MSRI) Berkeley, CA Postdoctoral Fellow Fall 2004 – Mentor: Bernd Sturmfels Texas A&M University College Station, TX Visiting Assistant Professor 2005 – 2007 – Mentor: Frank Sottile Appointments Colorado College Colorado Springs, CO Professor of Mathematics and Computer Science 2021 – Sam Houston State University Huntsville, TX Professor of Mathematics 2019 – 2021 Sam Houston State University Huntsville, TX Associate Department Chair Fall 2017 – 2021 Sam Houston State University Huntsville, TX Associate Professor of Mathematics 2013 – 2019 Statistical and Applied Mathematical Sciences Institute Research Triangle Park, NC SAMSI New Researcher fellowship Spring 2009 Sam Houston State University Huntsville, TX Assistant Professor of Mathematics 2007 – 2013 Virginia Bioinformatics Institute (Virginia Tech) Blacksburg, VA Graduate Research Assistant Spring 2004 Virginia Polytechnic Institute and State University Blacksburg, -
CAS (Computer Algebra System) Mathematica
CAS (Computer Algebra System) Mathematica- UML students can download a copy for free as part of the UML site license; see the course website for details From: Wikipedia 2/9/2014 A computer algebra system (CAS) is a software program that allows [one] to compute with mathematical expressions in a way which is similar to the traditional handwritten computations of the mathematicians and other scientists. The main ones are Axiom, Magma, Maple, Mathematica and Sage (the latter includes several computer algebras systems, such as Macsyma and SymPy). Computer algebra systems began to appear in the 1960s, and evolved out of two quite different sources—the requirements of theoretical physicists and research into artificial intelligence. A prime example for the first development was the pioneering work conducted by the later Nobel Prize laureate in physics Martin Veltman, who designed a program for symbolic mathematics, especially High Energy Physics, called Schoonschip (Dutch for "clean ship") in 1963. Using LISP as the programming basis, Carl Engelman created MATHLAB in 1964 at MITRE within an artificial intelligence research environment. Later MATHLAB was made available to users on PDP-6 and PDP-10 Systems running TOPS-10 or TENEX in universities. Today it can still be used on SIMH-Emulations of the PDP-10. MATHLAB ("mathematical laboratory") should not be confused with MATLAB ("matrix laboratory") which is a system for numerical computation built 15 years later at the University of New Mexico, accidentally named rather similarly. The first popular computer algebra systems were muMATH, Reduce, Derive (based on muMATH), and Macsyma; a popular copyleft version of Macsyma called Maxima is actively being maintained. -
SNC: a Cloud Service Platform for Symbolic-Numeric Computation Using Just-In-Time Compilation
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCC.2017.2656088, IEEE Transactions on Cloud Computing IEEE TRANSACTIONS ON CLOUD COMPUTING 1 SNC: A Cloud Service Platform for Symbolic-Numeric Computation using Just-In-Time Compilation Peng Zhang1, Member, IEEE, Yueming Liu1, and Meikang Qiu, Senior Member, IEEE and SQL. Other types of Cloud services include: Software as a Abstract— Cloud services have been widely employed in IT Service (SaaS) in which the software is licensed and hosted in industry and scientific research. By using Cloud services users can Clouds; and Database as a Service (DBaaS) in which managed move computing tasks and data away from local computers to database services are hosted in Clouds. While enjoying these remote datacenters. By accessing Internet-based services over lightweight and mobile devices, users deploy diversified Cloud great services, we must face the challenges raised up by these applications on powerful machines. The key drivers towards this unexploited opportunities within a Cloud environment. paradigm for the scientific computing field include the substantial Complex scientific computing applications are being widely computing capacity, on-demand provisioning and cross-platform interoperability. To fully harness the Cloud services for scientific studied within the emerging Cloud-based services environment computing, however, we need to design an application-specific [4-12]. Traditionally, the focus is given to the parallel scientific platform to help the users efficiently migrate their applications. In HPC (high performance computing) applications [6, 12, 13], this, we propose a Cloud service platform for symbolic-numeric where substantial effort has been given to the integration of computation– SNC. -
Analysis and Verification of Arithmetic Circuits Using Computer Algebra Approach
University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses March 2020 ANALYSIS AND VERIFICATION OF ARITHMETIC CIRCUITS USING COMPUTER ALGEBRA APPROACH TIANKAI SU University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/dissertations_2 Part of the VLSI and Circuits, Embedded and Hardware Systems Commons Recommended Citation SU, TIANKAI, "ANALYSIS AND VERIFICATION OF ARITHMETIC CIRCUITS USING COMPUTER ALGEBRA APPROACH" (2020). Doctoral Dissertations. 1868. https://doi.org/10.7275/15875490 https://scholarworks.umass.edu/dissertations_2/1868 This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. ANALYSIS AND VERIFICATION OF ARITHMETIC CIRCUITS USING COMPUTER ALGEBRA APPROACH A Dissertation Presented by TIANKAI SU Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY February 2020 Electrical and Computer Engineering c Copyright by Tiankai Su 2020 All Rights Reserved ANALYSIS AND VERIFICATION OF ARITHMETIC CIRCUITS USING COMPUTER ALGEBRA APPROACH A Dissertation Presented by TIANKAI SU Approved as to style and content by: Maciej Ciesielski, Chair George S. Avrunin, Member Daniel Holcomb, Member Weibo Gong, Member Christopher V. Hollot, Department Head Electrical and Computer Engineering ABSTRACT ANALYSIS AND VERIFICATION OF ARITHMETIC CIRCUITS USING COMPUTER ALGEBRA APPROACH FEBRUARY 2020 TIANKAI SU B.Sc., NORTHEAST DIANLI UNIVERSITY Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Maciej Ciesielski Despite a considerable progress in verification of random and control logic, ad- vances in formal verification of arithmetic designs have been lagging. -
SIGOPS Annual Report 2012
SIGOPS Annual Report 2012 Fiscal Year July 2012-June 2013 Submitted by Jeanna Matthews, SIGOPS Chair Overview SIGOPS is a vibrant community of people with interests in “operatinG systems” in the broadest sense, includinG topics such as distributed computing, storaGe systems, security, concurrency, middleware, mobility, virtualization, networkinG, cloud computinG, datacenter software, and Internet services. We sponsor a number of top conferences, provide travel Grants to students, present yearly awards, disseminate information to members electronically, and collaborate with other SIGs on important programs for computing professionals. Officers It was the second year for officers: Jeanna Matthews (Clarkson University) as Chair, GeorGe Candea (EPFL) as Vice Chair, Dilma da Silva (Qualcomm) as Treasurer and Muli Ben-Yehuda (Technion) as Information Director. As has been typical, elected officers agreed to continue for a second and final two- year term beginning July 2013. Shan Lu (University of Wisconsin) will replace Muli Ben-Yehuda as Information Director as of AuGust 2013. Awards We have an excitinG new award to announce – the SIGOPS Dennis M. Ritchie Doctoral Dissertation Award. SIGOPS has lonG been lackinG a doctoral dissertation award, such as those offered by SIGCOMM, Eurosys, SIGPLAN, and SIGMOD. This new award fills this Gap and also honors the contributions to computer science that Dennis Ritchie made durinG his life. With this award, ACM SIGOPS will encouraGe the creativity that Ritchie embodied and provide a reminder of Ritchie's leGacy and what a difference a person can make in the field of software systems research. The award is funded by AT&T Research and Alcatel-Lucent Bell Labs, companies that both have a strong connection to AT&T Bell Laboratories where Dennis Ritchie did his seminal work. -
Phillip B. Gibbons Curriculum Vitae
Phillip B. Gibbons Curriculum Vitae [email protected] http://cs.cmu.edu/~gibbons/ July 2020 Research Interests Research areas include big data, parallel computing, databases, cloud computing, sensor networks, distributed systems, and computer architecture. My publications span theory and systems, across a broad range of computer science and engineering (e.g., conference papers in APoCS, ATC, ESA, EuroSys, HPCA, ICML, IPDPS, ISCA, MICRO, NeurIPS, NSDI, OSDI, PACT, SoCC, SODA, SOSP, SPAA and VLDB since 2015). Education • University of California at Berkeley, Berkeley, California, 1984{1989. Ph.D. in Computer Science. Dissertation advisor: Richard M. Karp. • Dartmouth College, Hanover, New Hampshire, 1979{1983. B.A. in Mathematics. Graduated summa cum laude and Phi Beta Kappa. Professional Experience • Carnegie Mellon University, Pittsburgh, Pennsylvania. Professor, Computer Science Department, 2015{present. Professor, Electrical and Computer Engineering Department, 2015{present. Principal Investigator (PI or co-PI) for the following research projects: { Prescriptive Memory: Razing the semantic wall between applications and computer systems with heterogeneous compute and memories. { Asymmetric Memory: Write-efficient algorithms and systems, for settings (such as emerging non- volatile memories) where writes are significantly more costly than reads. { Big Learning Systems: Mapping out and exploring the space of large-scale machine learning from a systems' perspective. Recent focus on geo-distributed learning over non-IID data. Adjunct Professor, Computer Science Department, 2003{2015. Adjunct Associate Professor, Computer Science Department, 2000{2003. Visiting Professor, Computer Science Department, 2000. • Intel Labs Pittsburgh, Pittsburgh, Pennsylvania. Principal Research Scientist, 2001{2015. Principal Investigator for the Intel Science and Technology Center for Cloud Computing { A $11.5M research partnership with Carnegie Mellon, Georgia Tech, Princeton, UC Berkeley, and U. -
Polynomial Transformations of Tschirnhaus, Bring and Jerrard
ACM SIGSAM Bulletin, Vol 37, No. 3, September 2003 Polynomial Transformations of Tschirnhaus, Bring and Jerrard Victor S. Adamchik Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA [email protected] David J. Jeffrey Department of Applied Mathematics, The University of Western Ontario, London, Ontario, Canada [email protected] Abstract Tschirnhaus gave transformations for the elimination of some of the intermediate terms in a polynomial. His transformations were developed further by Bring and Jerrard, and here we describe all these transformations in modern notation. We also discuss their possible utility for polynomial solving, particularly with respect to the Mathematica poster on the solution of the quintic. 1 Introduction A recent issue of the BULLETIN contained a translation of the 1683 paper by Tschirnhaus [10], in which he proposed a method for solving a polynomial equation Pn(x) of degree n by transforming it into a polynomial Qn(y) which has a simpler form (meaning that it has fewer terms). Specifically, he extended the idea (which he attributed to Decartes) in which a polynomial of degree n is reduced or depressed (lovely word!) by removing its term in degree n ¡ 1. Tschirnhaus’s transformation is a polynomial substitution y = Tk(x), in which the degree of the transformation k < n can be selected. Tschirnhaus demonstrated the utility of his transformation by apparently solving the cubic equation in a way different from Cardano. Although Tschirnhaus’s work is described in modern books [9], later works by Bring [2, 3] and Jerrard [7, 6] have been largely forgotten. Here, we present the transformations in modern notation and make some comments on their utility. -
SIGOPS Annual Report 2011
SIGOPS Annual Report 2011 Fiscal Year July 2010-June 2011 Submitted by Doug Terry, SIGOPS Chair Overview SIGOPS is a vibrant community of people with interests in “operating systems” in the broadest sense, including topics such as distributed computing, storage systems, security, concurrency, middleware, mobility, virtualization, networking, cloud computing, datacenter software, and Internet services. We sponsor a number of top conferences, provide travel grants to students, present yearly awards, disseminate information to members electronically, and collaborate with other SIGs on important programs for computing professionals. Notable activities from the past year include: Elections were held for new officers, and Jeanna Matthews was elected as the new SIGOPS Chair, George Candea as Vice Chair, and Dilma da Silva as Treasurer. They will serve two-year terms from July 1, 2011 through June 30, 2013. The past officers (Doug Terry as Chair, Frank Bellosa as Vice Chair and Jeanna Matthews as Treasurer) completed their 4 years of service on June 30. Planning for the next ACM Symposium on Operating Systems (SOSP), which is scheduled for October 2011 in Cascais, Portugal, has been largely completed by Ted Wobber, the General Chair, and Peter Druschel, the Program Chair. The Second ACM Symposium on Cloud Computing (SOCC), which is co-sponsored between SIGOPS and SIGMOD, is being co-located with SOSP in Portugal. The first SIGOPS Asia-Pacific Workshop on Systems (APSys) was held on August 30, 2010 in New Delhi, India, immediately before the SIGCOMM 2010 conference. And the second instance of this workshop is being held in Shanghai, China, in July 2011. ACM released the first version of the Cloud Computing Tech Pack, which was produced by Doug Terry, the SIGOPS Chair.