Ternary forms with lots of zeros
|
|
- Alexina Crawford
- 5 years ago
- Views:
Transcription
1 Ternary forms with lots of zeros Bruce Reznick University of Illinois at Urbana-Champaign Conference/Workshop on Inverse Moment Problems, IMS National University of Singapore, Dec. 19, 2013
2 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman)
3 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones
4 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones Relation to moments
5 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones Relation to moments Hilbert 1893
6 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones Relation to moments Hilbert 1893 Robinson 1969
7 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones Relation to moments Hilbert 1893 Robinson 1969 w/ Choi-Lam 1980
8 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones Relation to moments Hilbert 1893 Robinson 1969 w/ Choi-Lam 1980 Arxiv paper 2007
9 17 Outline of the talk Statement of the problem and the conjectures (joint with Greg Blekherman) Relation to cones Relation to moments Hilbert 1893 Robinson 1969 w/ Choi-Lam 1980 Arxiv paper 2007 New material (joint with Greg Blekherman and??)
10 In this table, we see, in order: 2r, r 2, (2r 1)(2r 2) 2, their maximum, and then 3 2r(r 1) + 1. The smaller of the last two is the upper bound on Z(p) for p P 3,2r.
11 Let I(n, d) = {(i 1,..., i n ) : 0 i k Z, i k = d} and for i I(n, d) write the multinomial coefficient c(i) = d! i 1!...i. Write n! x i = x i xn in and for a real form p of degree d in n variables, scale the coefficients by: p = c(i)a(p; i)x i i I(n,d) Then the Fischer inner product is defined by [p, q] = c(i)a(p; i)a(q; i). i I(n,d) For α R n define (α ) d = (α x) d, then it is easy to see that [p, (α ) d ] = p(α).
12 It is not so hard to see from this that P n,2r and Q n,2r are dual cones. The dual cone to Σ n,2r is easily understood. If p Σ n,2r, then [p, h 2 ] 0 for all h of degree r. Write a general form of degree r with the coefficients as variables h(x) = a(p; l+l )t(l)t(l ) := H p (t) l I (n,r) t(l)x l = [p, h 2 ] = l This construction was done by Sylvester for ternary quartics; he called it the catalecticant. It s important to note that ( H (α ) 2r (t) = α l t(l) and so the rank of H p is a lower bound on the width of p. When P n,2r = Σ n,2r, the duals are equal and the two are the same. In general, this doesn t happen for the other cases. l l ) 2
13 Suppose p(x, y, z) = i I (3,d) d! i!j!k! a(p; (i, j, k))x i y j z k. Then N N p(x, y, z) = (a t x +b t y +c t z) d a(p; (i, j, k)) = atb i tc j t k t=1 t=1
14 Suppose p(x, y, z) = i I (3,d) d! i!j!k! a(p; (i, j, k))x i y j z k. Then N N p(x, y, z) = (a t x +b t y +c t z) d a(p; (i, j, k)) = atb i tc j t k t=1 t=1 If c t 0, this can be rewritten as a(p; (i, j, k)) = N ct d t=1 ( at ) i ( bt c t c t ) j
15 Suppose p(x, y, z) = i I (3,d) d! i!j!k! a(p; (i, j, k))x i y j z k. Then N N p(x, y, z) = (a t x +b t y +c t z) d a(p; (i, j, k)) = atb i tc j t k t=1 t=1 If c t 0, this can be rewritten as a(p; (i, j, k)) = N t=1 c d t or a(p; (i, j, k)) = ( at ) i ( bt c t c t X i Y j dµ, ) j where µ has N masses of weight c d t at ( at c t, bt c t ).
16 ContourPlot x^2 1 ^2 x^2 9 ^2 y^2 1 ^2 y^2 9 ^2 10, x, 4, 4, y, 4,
17
18
19 Out[751]= 6075 x x 4 y x 2 y 6 In[752]:= Out[752]= 23 23
20 In[756]:= ContourPlot 751. z , x, 4, 4, 4 2 Out[756]=
21 Out[748]= 25 x 8 72 x 6 y x 5 y x 4 y x 3 y y z 9720 x z 1440 x y z 9720 y z 8100 z
22 In[750]:= ContourPlot 748. z 1 100, x, 4, 4, 4 2 Out[750]=
23 Thank you for your patience.
Ranks of Real Symmetric Tensors
Ranks of Real Symmetric Tensors Greg Blekherman SIAM AG 2013 Algebraic Geometry of Tensor Decompositions Real Symmetric Tensor Decompositions Let f be a form of degree d in R[x 1,..., x n ]. We would like
More informationRepresentations of Positive Polynomials: Theory, Practice, and
Representations of Positive Polynomials: Theory, Practice, and Applications Dept. of Mathematics and Computer Science Emory University, Atlanta, GA Currently: National Science Foundation Temple University
More informationForms as sums of powers of lower degree forms
Bruce Reznick University of Illinois at Urbana-Champaign SIAM Conference on Applied Algebraic Geometry Algebraic Geometry of Tensor Decompositions Fort Collins, Colorado August 2, 2013 Let H d (C n ) denote
More informationSOME NEW CANONICAL FORMS FOR POLYNOMIALS
SOME NEW CANONICAL FORMS FOR POLYNOMIALS BRUCE REZNICK Abstract. We give some new canonical representations for forms over C. For example, a general binary quartic form can be written as the square of
More informationarxiv:math/ v1 [math.ag] 10 Jun 2003
arxiv:math/0306163v1 [math.ag] 10 Jun 2003 ON THE ABSENCE OF UNIFORM DENOMINATORS IN HILBERT S 17TH PROBLEM BRUCE REZNICK Abstract. Hilbert showedthat formost(n,m) thereexist psdformsp(x 1,...,x n ) of
More informationOptimal Tap Settings for Voltage Regulation Transformers in Distribution Networks
Optimal Tap Settings for Voltage Regulation Transformers in Distribution Networks Brett A. Robbins Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign May 9, 2014
More informationThe Pythagoras numbers of projective varieties.
The Pythagoras numbers of projective varieties. Grigoriy Blekherman (Georgia Tech, USA) Rainer Sinn (FU Berlin, Germany) Gregory G. Smith (Queen s University, Canada) Mauricio Velasco (Universidad de los
More informationSample Geometry. Edps/Soc 584, Psych 594. Carolyn J. Anderson
Sample Geometry Edps/Soc 584, Psych 594 Carolyn J. Anderson Department of Educational Psychology I L L I N O I S university of illinois at urbana-champaign c Board of Trustees, University of Illinois Spring
More informationO.K. But what if the chicken didn t have access to a teleporter.
The intermediate value theorem, and performing algebra on its. This is a dual topic lecture. : The Intermediate value theorem First we should remember what it means to be a continuous function: A function
More informationHYPERBOLIC POLYNOMIALS, INTERLACERS AND SUMS OF SQUARES
HYPERBOLIC POLYNOMIALS, INTERLACERS AND SUMS OF SQUARES DANIEL PLAUMANN Universität Konstanz joint work with Mario Kummer (U. Konstanz) Cynthia Vinzant (U. of Michigan) Out[121]= POLYNOMIAL OPTIMISATION
More informationSection 0.2 & 0.3 Worksheet. Types of Functions
MATH 1142 NAME Section 0.2 & 0.3 Worksheet Types of Functions Now that we have discussed what functions are and some of their characteristics, we will explore different types of functions. Section 0.2
More informationEffectivity Issues and Results for Hilbert 17 th Problem
Effectivity Issues and Results for Hilbert 17 th Problem Marie-Françoise Roy Université de Rennes 1, France joint work with Henri Lombardi Université de Franche-Comté, France and Daniel Perrucci Universidad
More informationIncentive-Based Pricing for Network Games with Complete and Incomplete Information
Incentive-Based Pricing for Network Games with Complete and Incomplete Information Hongxia Shen and Tamer Başar Coordinated Science Laboratory University of Illinois at Urbana-Champaign hshen1, tbasar@control.csl.uiuc.edu
More informationRoots and Coefficients Polynomials Preliminary Maths Extension 1
Preliminary Maths Extension Question If, and are the roots of x 5x x 0, find the following. (d) (e) Question If p, q and r are the roots of x x x 4 0, evaluate the following. pq r pq qr rp p q q r r p
More informationProperties of Commutators and Schroedinger Operators and Applications to Quantum Computing
International Journal of Engineering and Advanced Research Technology (IJEART) Properties of Commutators and Schroedinger Operators and Applications to Quantum Computing N. B. Okelo Abstract In this paper
More informationTensor decomposition and moment matrices
Tensor decomposition and moment matrices J. Brachat (Ph.D.) & B. Mourrain GALAAD, INRIA Méditerranée, Sophia Antipolis Bernard.Mourrain@inria.fr SIAM AAG, Raleigh, NC, Oct. -9, 211 Collaboration with A.
More informationNoisy channel communication
Information Theory http://www.inf.ed.ac.uk/teaching/courses/it/ Week 6 Communication channels and Information Some notes on the noisy channel setup: Iain Murray, 2012 School of Informatics, University
More informationGlobal Optimization of Polynomials
Semidefinite Programming Lecture 9 OR 637 Spring 2008 April 9, 2008 Scribe: Dennis Leventhal Global Optimization of Polynomials Recall we were considering the problem min z R n p(z) where p(z) is a degree
More informationJointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs
Jointly Clustering Rows and Columns of Binary Matrices: Algorithms and Trade-offs Jiaming Xu Joint work with Rui Wu, Kai Zhu, Bruce Hajek, R. Srikant, and Lei Ying University of Illinois, Urbana-Champaign
More informationON SUM OF SQUARES DECOMPOSITION FOR A BIQUADRATIC MATRIX FUNCTION
Annales Univ. Sci. Budapest., Sect. Comp. 33 (2010) 273-284 ON SUM OF SQUARES DECOMPOSITION FOR A BIQUADRATIC MATRIX FUNCTION L. László (Budapest, Hungary) Dedicated to Professor Ferenc Schipp on his 70th
More informationHilbert s Nullstellensatz
Hilbert s Nullstellensatz An Introduction to Algebraic Geometry Scott Sanderson Department of Mathematics Williams College April 6, 2013 Introduction My talk today is on Hilbert s Nullstellensatz, a foundational
More informationSOS TENSOR DECOMPOSITION: THEORY AND APPLICATIONS
SOS TENSOR DECOMPOSITION: THEORY AND APPLICATIONS HAIBIN CHEN, GUOYIN LI, AND LIQUN QI Abstract. In this paper, we examine structured tensors which have sum-of-squares (SOS) tensor decomposition, and study
More informationThe highest degree term is x $, therefore the function is degree 4 (quartic) c) What are the x-intercepts?
L3 1.3 Factored Form Polynomial Functions Lesson MHF4U Jensen In this section, you will investigate the relationship between the factored form of a polynomial function and the x-intercepts of the corresponding
More informationLecture : More Derivative Rules MTH 124
Today we investigate how to differentiate products of functions and quotients of functions. For example how can we go about differentiating (x + 4)(x 2 + 5) or x+4? The rules we will x 2 +5 learn today
More informationMathematics and Computer Science
Technical Report TR-2005-006 A New Proof of Hilbert s Theorem on Ternary Quartics by Victoria Powers, Bruce Reznick, Claus Scheiderer, Frank Sottile Mathematics and Computer Science EMORY UNIVERSITY A
More informationON THE LENGTH OF BINARY FORMS
ON THE LENGTH OF BINARY FORMS BRUCE REZNICK Abstract. The K-length of a form f in K[x 1,..., x n ], K C, is the smallest number of d-th powers of linear forms of which f is a K-linear combination. We present
More information2, or x 5, 3 x 0, x 2
Pre-AP Algebra 2 Lesson 2 End Behavior and Polynomial Inequalities Objectives: Students will be able to: use a number line model to sketch polynomials that have repeated roots. use a number line model
More informationRecursions and Colored Hilbert Schemes
Recursions and Colored Hilbert Schemes Anna Brosowsky [Joint Work With: Nathaniel Gillman, Murray Pendergrass] [Mentor: Dr. Amin Gholampour] Cornell University 24 September 2016 WiMiN Smith College 1/26
More informationCS8803: Statistical Techniques in Robotics Byron Boots. Hilbert Space Embeddings
CS8803: Statistical Techniques in Robotics Byron Boots Hilbert Space Embeddings 1 Motivation CS8803: STR Hilbert Space Embeddings 2 Overview Multinomial Distributions Marginal, Joint, Conditional Sum,
More informationPredator - Prey Model Trajectories and the nonlinear conservation law
Predator - Prey Model Trajectories and the nonlinear conservation law James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University October 28, 2013 Outline
More informationALGEBRAIC BOUNDARIES OF HILBERT S SOS CONES
ALGEBRAIC BOUNDARIES OF HILBERT S SOS CONES GRIGORIY BLEKHERMAN, JONATHAN HAUENSTEIN, JOHN CHRISTIAN OTTEM, KRISTIAN RANESTAD AND BERND STURMFELS Abstract. We study the geometry underlying the difference
More informationDUALITY FOR POSITIVE LINEAR MAPS IN MATRIX ALGEBRAS
MATH. SCAND. 86 (2000), 130^142 DUALITY FOR POSITIVE LINEAR MAPS IN MATRIX ALGEBRAS MYOUNG-HOE EOM AND SEUNG-HYEOK KYE Abstract We characterize extreme rays of the dual cone of the cone consisting of all
More informationSemidefinite and Second Order Cone Programming Seminar Fall 2001 Lecture 2
Semidefinite and Second Order Cone Programming Seminar Fall 2001 Lecture 2 Instructor: Farid Alizadeh Scribe: Xuan Li 9/17/2001 1 Overview We survey the basic notions of cones and cone-lp and give several
More informationOptimization over Polynomials with Sums of Squares and Moment Matrices
Optimization over Polynomials with Sums of Squares and Moment Matrices Monique Laurent Centrum Wiskunde & Informatica (CWI), Amsterdam and University of Tilburg Positivity, Valuations and Quadratic Forms
More informationMA Ordinary Differential Equations
MA 108 - Ordinary Differential Equations Santanu Dey Department of Mathematics, Indian Institute of Technology Bombay, Powai, Mumbai 76 dey@math.iitb.ac.in March 26, 2014 Outline of the lecture Method
More informationLECTURE 5, FRIDAY
LECTURE 5, FRIDAY 20.02.04 FRANZ LEMMERMEYER Before we start with the arithmetic of elliptic curves, let us talk a little bit about multiplicities, tangents, and singular points. 1. Tangents How do we
More informationCapacity of a channel Shannon s second theorem. Information Theory 1/33
Capacity of a channel Shannon s second theorem Information Theory 1/33 Outline 1. Memoryless channels, examples ; 2. Capacity ; 3. Symmetric channels ; 4. Channel Coding ; 5. Shannon s second theorem,
More informationA Symposium to Mark the Seventieth Birthday of Professor James C. Beidleman, to Honor his Mathematical Achievements, and to Celebrate his Friendship.
A Symposium to Mark the Seventieth Birthday of Professor James C. Beidleman, to Honor his Mathematical Achievements, and to Celebrate his Friendship. Program & s Organizers Ben Brewster Lyn Lemieux Matthew
More informationThe number of singular vector tuples and approximation of symmetric tensors / 13
The number of singular vector tuples and approximation of symmetric tensors Shmuel Friedland Univ. Illinois at Chicago Colloquium NYU Courant May 12, 2014 Joint results with Giorgio Ottaviani and Margaret
More informationFunction Space and Convergence Types
Function Space and Convergence Types PHYS 500 - Southern Illinois University November 1, 2016 PHYS 500 - Southern Illinois University Function Space and Convergence Types November 1, 2016 1 / 7 Recall
More informationKevin James. MTHSC 206 Section 12.5 Equations of Lines and Planes
MTHSC 206 Section 12.5 Equations of Lines and Planes Definition A line in R 3 can be described by a point and a direction vector. Given the point r 0 and the direction vector v. Any point r on the line
More informationNAME DATE PERIOD. Operations with Polynomials. Review Vocabulary Evaluate each expression. (Lesson 1-1) 3a 2 b 4, given a = 3, b = 2
5-1 Operations with Polynomials What You ll Learn Skim the lesson. Predict two things that you expect to learn based on the headings and the Key Concept box. 1. Active Vocabulary 2. Review Vocabulary Evaluate
More informationAre There Sixth Order Three Dimensional PNS Hankel Tensors?
Are There Sixth Order Three Dimensional PNS Hankel Tensors? Guoyin Li Liqun Qi Qun Wang November 17, 014 Abstract Are there positive semi-definite PSD) but not sums of squares SOS) Hankel tensors? If the
More informationA Friendly Guide to the Frame Theory. and Its Application to Signal Processing
A Friendly uide to the Frame Theory and Its Application to Signal Processing inh N. Do Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign www.ifp.uiuc.edu/ minhdo
More informationStructure in Mixed Integer Conic Optimization: From Minimal Inequalities to Conic Disjunctive Cuts
Structure in Mixed Integer Conic Optimization: From Minimal Inequalities to Conic Disjunctive Cuts Fatma Kılınç-Karzan Tepper School of Business Carnegie Mellon University Joint work with Sercan Yıldız
More informationc 2014 Krishna Kalyan Medarametla
c 2014 Krishna Kalyan Medarametla COMPARISON OF TWO NONLINEAR FILTERING TECHNIQUES - THE EXTENDED KALMAN FILTER AND THE FEEDBACK PARTICLE FILTER BY KRISHNA KALYAN MEDARAMETLA THESIS Submitted in partial
More informationZhi-Wei Sun. Department of Mathematics Nanjing University Nanjing , P. R. China
A talk given at National Cheng Kung Univ. (2007-06-28). SUMS OF SQUARES AND TRIANGULAR NUMBERS, AND RADO NUMBERS FOR LINEAR EQUATIONS Zhi-Wei Sun Department of Mathematics Nanjing University Nanjing 210093,
More informationApplications of geometry to modular representation theory. Julia Pevtsova University of Washington, Seattle
Applications of geometry to modular representation theory Julia Pevtsova University of Washington, Seattle October 25, 2014 G - finite group, k - field. Study Representation theory of G over the field
More informationEquivariant semidefinite lifts and sum of squares hierarchies
Equivariant semidefinite lifts and sum of squares hierarchies Pablo A. Parrilo Laboratory for Information and Decision Systems Electrical Engineering and Computer Science Massachusetts Institute of Technology
More informationCorrelation Detection and an Operational Interpretation of the Rényi Mutual Information
Correlation Detection and an Operational Interpretation of the Rényi Mutual Information Masahito Hayashi 1, Marco Tomamichel 2 1 Graduate School of Mathematics, Nagoya University, and Centre for Quantum
More informationDefinite versus Indefinite Linear Algebra. Christian Mehl Institut für Mathematik TU Berlin Germany. 10th SIAM Conference on Applied Linear Algebra
Definite versus Indefinite Linear Algebra Christian Mehl Institut für Mathematik TU Berlin Germany 10th SIAM Conference on Applied Linear Algebra Monterey Bay Seaside, October 26-29, 2009 Indefinite Linear
More informationSPECTRAHEDRA. Bernd Sturmfels UC Berkeley
SPECTRAHEDRA Bernd Sturmfels UC Berkeley GAeL Lecture I on Convex Algebraic Geometry Coimbra, Portugal, Monday, June 7, 2010 Positive Semidefinite Matrices For a real symmetric n n-matrix A the following
More informationGeorgia Department of Education Common Core Georgia Performance Standards Framework CCGPS Advanced Algebra Unit 2
Polynomials Patterns Task 1. To get an idea of what polynomial functions look like, we can graph the first through fifth degree polynomials with leading coefficients of 1. For each polynomial function,
More informationMore On Exponential Functions, Inverse Functions and Derivative Consequences
More On Exponential Functions, Inverse Functions and Derivative Consequences James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University January 10, 2019
More informationSixth Term Examination Papers 9475 MATHEMATICS 3 THURSDAY 22 JUNE 2017
Sixth Term Examination Papers 9475 MATHEMATICS 3 THURSDAY 22 JUNE 207 INSTRUCTIONS TO CANDIDATES AND INFORMATION FOR CANDIDATES six six Calculators are not permitted. Please wait to be told you may begin
More informationDot Products, Transposes, and Orthogonal Projections
Dot Products, Transposes, and Orthogonal Projections David Jekel November 13, 2015 Properties of Dot Products Recall that the dot product or standard inner product on R n is given by x y = x 1 y 1 + +
More information2.4 Hilbert Spaces. Outline
2.4 Hilbert Spaces Tom Lewis Spring Semester 2017 Outline Hilbert spaces L 2 ([a, b]) Orthogonality Approximations Definition A Hilbert space is an inner product space which is complete in the norm defined
More informationOne-Dimensional Line Schemes Michaela Vancliff
One-Dimensional Line Schemes Michaela Vancliff University of Texas at Arlington, USA http://www.uta.edu/math/vancliff/r vancliff@uta.edu Partial support from NSF DMS-1302050. Motivation Throughout, k =
More informationMarch 8, 2010 MATH 408 FINAL EXAM SAMPLE
March 8, 200 MATH 408 FINAL EXAM SAMPLE EXAM OUTLINE The final exam for this course takes place in the regular course classroom (MEB 238) on Monday, March 2, 8:30-0:20 am. You may bring two-sided 8 page
More informationOutline. Binomial, Multinomial, Normal, Beta, Dirichlet. Posterior mean, MAP, credible interval, posterior distribution
Outline A short review on Bayesian analysis. Binomial, Multinomial, Normal, Beta, Dirichlet Posterior mean, MAP, credible interval, posterior distribution Gibbs sampling Revisit the Gaussian mixture model
More information[Limits at infinity examples] Example. The graph of a function y = f(x) is shown below. Compute lim f(x) and lim f(x).
[Limits at infinity eamples] Eample. The graph of a function y = f() is shown below. Compute f() and f(). y -8 As you go to the far right, the graph approaches y =, so f() =. As you go to the far left,
More informationRuppert matrix as subresultant mapping
Ruppert matrix as subresultant mapping Kosaku Nagasaka Kobe University JAPAN This presentation is powered by Mathematica 6. 09 : 29 : 35 16 Ruppert matrix as subresultant mapping Prev Next 2 CASC2007slideshow.nb
More informationProbabilistic Graphical Models
Probabilistic Graphical Models Lecture 9: Variational Inference Relaxations Volkan Cevher, Matthias Seeger Ecole Polytechnique Fédérale de Lausanne 24/10/2011 (EPFL) Graphical Models 24/10/2011 1 / 15
More informationProbability and combinatorics
Texas A&M University May 1, 2012 Probability spaces. (Λ, M, P ) = measure space. Probability space: P a probability measure, P (Λ) = 1. Probability spaces. (Λ, M, P ) = measure space. Probability space:
More informationMAT 578 FUNCTIONAL ANALYSIS EXERCISES
MAT 578 FUNCTIONAL ANALYSIS EXERCISES JOHN QUIGG Exercise 1. Prove that if A is bounded in a topological vector space, then for every neighborhood V of 0 there exists c > 0 such that tv A for all t > c.
More informationPerturbation theory for eigenvalues of Hermitian pencils. Christian Mehl Institut für Mathematik TU Berlin, Germany. 9th Elgersburg Workshop
Perturbation theory for eigenvalues of Hermitian pencils Christian Mehl Institut für Mathematik TU Berlin, Germany 9th Elgersburg Workshop Elgersburg, March 3, 2014 joint work with Shreemayee Bora, Michael
More informationLegendre s Equation. PHYS Southern Illinois University. October 18, 2016
Legendre s Equation PHYS 500 - Southern Illinois University October 18, 2016 PHYS 500 - Southern Illinois University Legendre s Equation October 18, 2016 1 / 11 Legendre s Equation Recall We are trying
More informationProjective Varieties. Chapter Projective Space and Algebraic Sets
Chapter 1 Projective Varieties 1.1 Projective Space and Algebraic Sets 1.1.1 Definition. Consider A n+1 = A n+1 (k). The set of all lines in A n+1 passing through the origin 0 = (0,..., 0) is called the
More informationLOWER BOUNDS FOR A POLYNOMIAL IN TERMS OF ITS COEFFICIENTS
LOWER BOUNDS FOR A POLYNOMIAL IN TERMS OF ITS COEFFICIENTS MEHDI GHASEMI AND MURRAY MARSHALL Abstract. Recently Lasserre [6] gave a sufficient condition in terms of the coefficients for a polynomial f
More informationTensor decomposition and tensor rank
from the point of view of Classical Algebraic Geometry RTG Workshop Tensors and their Geometry in High Dimensions (September 26-29, 2012) UC Berkeley Università di Firenze Content of the three talks Wednesday
More informationPOLE PLACEMENT. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 19
POLE PLACEMENT Sadegh Bolouki Lecture slides for ECE 515 University of Illinois, Urbana-Champaign Fall 2016 S. Bolouki (UIUC) 1 / 19 Outline 1 State Feedback 2 Observer 3 Observer Feedback 4 Reduced Order
More informationFractional Fischer decomposition, the ternary case
Fractional Fischer decomposition, the ternary case Aurineide Fonseca Department of Mathematics, University of Aveiro, Portugal & Federal University of Piauí, Brazil aurineidefonseca@ufpi.edu.br MOIMA06-06/06/
More informationShort note on compact operators - Monday 24 th March, Sylvester Eriksson-Bique
Short note on compact operators - Monday 24 th March, 2014 Sylvester Eriksson-Bique 1 Introduction In this note I will give a short outline about the structure theory of compact operators. I restrict attention
More informationMeasures, orthogonal polynomials, and continued fractions. Michael Anshelevich
Measures, orthogonal polynomials, and continued fractions Michael Anshelevich November 7, 2008 MEASURES AND ORTHOGONAL POLYNOMIALS. MEASURES AND ORTHOGONAL POLYNOMIALS. µ a positive measure on R. 2 MEASURES
More informationSymmetries and Polynomials
Symmetries and Polynomials Aaron Landesman and Apurva Nakade June 30, 2018 Introduction In this class we ll learn how to solve a cubic. We ll also sketch how to solve a quartic. We ll explore the connections
More informationSixty-Four Curves of Degree Six
Sixty-Four Curves of Degree Six Bernd Sturmfels MPI Leipzig and UC Berkeley with Nidhi Kaihnsa, Mario Kummer, Daniel Plaumann and Mahsa Sayyary 1 / 24 Poset 2 / 24 Hilbert s 16th Problem Classify all real
More informationThe degree of the polynomial function is n. We call the term the leading term, and is called the leading coefficient. 0 =
Math 1310 A polynomial function is a function of the form = + + +...+ + where 0,,,, are real numbers and n is a whole number. The degree of the polynomial function is n. We call the term the leading term,
More informationThe Gram matrix in inner product modules over C -algebras
The Gram matrix in inner product modules over C -algebras Ljiljana Arambašić (joint work with D. Bakić and M.S. Moslehian) Department of Mathematics University of Zagreb Applied Linear Algebra May 24 28,
More informationApproximate Kernel Methods
Lecture 3 Approximate Kernel Methods Bharath K. Sriperumbudur Department of Statistics, Pennsylvania State University Machine Learning Summer School Tübingen, 207 Outline Motivating example Ridge regression
More informationMeasures, orthogonal polynomials, and continued fractions. Michael Anshelevich
Measures, orthogonal polynomials, and continued fractions Michael Anshelevich November 7, 2008 MEASURES AND ORTHOGONAL POLYNOMIALS. µ a positive measure on R. A linear functional µ[p ] = P (x) dµ(x), µ
More informationLINEAR MMSE ESTIMATION
LINEAR MMSE ESTIMATION TERM PAPER FOR EE 602 STATISTICAL SIGNAL PROCESSING By, DHEERAJ KUMAR VARUN KHAITAN 1 Introduction Linear MMSE estimators are chosen in practice because they are simpler than the
More informationConvolution and Linear Systems
CS 450: Introduction to Digital Signal and Image Processing Bryan Morse BYU Computer Science Introduction Analyzing Systems Goal: analyze a device that turns one signal into another. Notation: f (t) g(t)
More informationBMT 2016 Orthogonal Polynomials 12 March Welcome to the power round! This year s topic is the theory of orthogonal polynomials.
Power Round Welcome to the power round! This year s topic is the theory of orthogonal polynomials. I. You should order your papers with the answer sheet on top, and you should number papers addressing
More informationGeometry of log-concave Ensembles of random matrices
Geometry of log-concave Ensembles of random matrices Nicole Tomczak-Jaegermann Joint work with Radosław Adamczak, Rafał Latała, Alexander Litvak, Alain Pajor Cortona, June 2011 Nicole Tomczak-Jaegermann
More informationThe Method of Types and Its Application to Information Hiding
The Method of Types and Its Application to Information Hiding Pierre Moulin University of Illinois at Urbana-Champaign www.ifp.uiuc.edu/ moulin/talks/eusipco05-slides.pdf EUSIPCO Antalya, September 7,
More informationSemidefinite programming lifts and sparse sums-of-squares
1/15 Semidefinite programming lifts and sparse sums-of-squares Hamza Fawzi (MIT, LIDS) Joint work with James Saunderson (UW) and Pablo Parrilo (MIT) Cornell ORIE, October 2015 Linear optimization 2/15
More informationMathematical Induction Again
Mathematical Induction Again James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University January 2, 207 Outline Mathematical Induction 2 Simple POMI Examples
More informationMathematical Induction Again
Mathematical Induction Again James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University January 12, 2017 Outline Mathematical Induction Simple POMI Examples
More informationPolynomial Regression and Regularization
Polynomial Regression and Regularization Administrivia o If you still haven t enrolled in Moodle yet Enrollment key on Piazza If joined course recently, email me to get added to Piazza o Homework 1 posted
More informationOrdinary Differential Equations (ODEs) Background. Video 17
Ordinary Differential Equations (ODEs) Background Video 17 Daniel J. Bodony Department of Aerospace Engineering University of Illinois at Urbana-Champaign In this video you will learn... 1 What ODEs are
More informationClassical codes for quantum broadcast channels. arxiv: Ivan Savov and Mark M. Wilde School of Computer Science, McGill University
Classical codes for quantum broadcast channels arxiv:1111.3645 Ivan Savov and Mark M. Wilde School of Computer Science, McGill University International Symposium on Information Theory, Boston, USA July
More informationLecture 7: September 17
10-725: Optimization Fall 2013 Lecture 7: September 17 Lecturer: Ryan Tibshirani Scribes: Serim Park,Yiming Gu 7.1 Recap. The drawbacks of Gradient Methods are: (1) requires f is differentiable; (2) relatively
More informationA positive definite polynomial Hessian that does not factor
A positive definite polynomial Hessian that does not factor The MIT Faculty has made this article openly available Please share how this access benefits you Your story matters Citation As Published Publisher
More informationHilbert s 17th Problem to Semidefinite Programming & Convex Algebraic Geometry
Hilbert s 17th Problem to Semidefinite Programming & Convex Algebraic Geometry Rekha R. Thomas University of Washington, Seattle References Monique Laurent, Sums of squares, moment matrices and optimization
More informationDSOS/SDOS Programming: New Tools for Optimization over Nonnegative Polynomials
DSOS/SDOS Programming: New Tools for Optimization over Nonnegative Polynomials Amir Ali Ahmadi Princeton University Dept. of Operations Research and Financial Engineering (ORFE) Joint work with: Anirudha
More informationExact SDP Relaxations for Classes of Nonlinear Semidefinite Programming Problems
Exact SDP Relaxations for Classes of Nonlinear Semidefinite Programming Problems V. Jeyakumar and G. Li Revised Version:August 31, 2012 Abstract An exact semidefinite linear programming (SDP) relaxation
More informationSemidefinite Programming
Semidefinite Programming Basics and SOS Fernando Mário de Oliveira Filho Campos do Jordão, 2 November 23 Available at: www.ime.usp.br/~fmario under talks Conic programming V is a real vector space h, i
More informationLinear Models Review
Linear Models Review Vectors in IR n will be written as ordered n-tuples which are understood to be column vectors, or n 1 matrices. A vector variable will be indicted with bold face, and the prime sign
More informationSupport Vector Machines
Wien, June, 2010 Paul Hofmarcher, Stefan Theussl, WU Wien Hofmarcher/Theussl SVM 1/21 Linear Separable Separating Hyperplanes Non-Linear Separable Soft-Margin Hyperplanes Hofmarcher/Theussl SVM 2/21 (SVM)
More informatione socle degrees 0 2: 1 1 4: 7 2 9: : : 12.
Socle degrees of Frobenius powers Lecture January 8, 26 talk by A. Kustin I will talk about recent joint work with Adela Vraciu. A preprint is available if you want all of the details. I will only talk
More information