Describing algebraic varieties to a computer

Size: px
Start display at page:

Download "Describing algebraic varieties to a computer"

Transcription

1 Describing algebraic varieties to a computer Jose Israel Rodriguez University of Chicago String Pheno 2017 July 6, 2017

2 Outline Degree of a curve Component membership test Monodromy and trace test Witness sets References: Multiprojective witness sets and a trace test Jonathan Hauenstein and [-] Trace test Anton Leykin, [-], Frank Sottile The maximum likelihood degree of mixtures of independence models [-] and Botong Wang (Counting critical points)

3 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

4 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

5 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

6 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

7 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

8 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

9 Testing membership Intersect a curve V with a hyperplane L. Consider a curve V C n (may be reducible). [Draw] For a general hyperplane L of C n, [Draw] the degree of V is the cardinality of L V. Question (Membership): Given a point B C n, is B in the V? If we know that equations F = 0 define V, then check F (B) = 0. Membership test: Let M denote a general hyperplane through B. Deform L M to determine M V. Check if B M V. If B is not in M V then B is not in V by definition of degree. [Draw] But what about restricting to components (lacking equations)???? Problem: How to sort the points V L by irreducible component?

10 Monodromy Deforming L to itself is a monodromy used to sort the points in V L. Repeatedly deform L to itself to find points in L V that are on the same component. In example C s go to C s and D s go to D s. Illustrate on circle (Short move then rotation). Problem: No stopping criteria to say that we are done sorting. (Reminder: we are working over the complex numbers.) Use the trace (coordinate-wise average) of a set of points.

11 Monodromy Deforming L to itself is a monodromy used to sort the points in V L. Repeatedly deform L to itself to find points in L V that are on the same component. In example C s go to C s and D s go to D s. Illustrate on circle (Short move then rotation). Problem: No stopping criteria to say that we are done sorting. (Reminder: we are working over the complex numbers.) Use the trace (coordinate-wise average) of a set of points.

12 Trace The trace of a set of points S is the coordinate-wise average. Let L t denote a family of hyperplanes defined by H + t = 0. Let X be an irreducible component of V C n. Suppose S X L t. [Trace test] The trace of S is affine linear in t if and only if S = X L t. Numerical irreducible decomposition using projections from points on the components by A.J. Sommese, J. Verschelde, and C.W. Wampler. [Draw to illustrate affine linear in t] Not limited to curves. Take L to be defined by linear equations H 1,H 2,...,H dimv.

13 Trace The trace of a set of points S is the coordinate-wise average. Let L t denote a family of hyperplanes defined by H + t = 0. Let X be an irreducible component of V C n. Suppose S X L t. [Trace test] The trace of S is affine linear in t if and only if S = X L t. Numerical irreducible decomposition using projections from points on the components by A.J. Sommese, J. Verschelde, and C.W. Wampler. [Draw to illustrate affine linear in t] Not limited to curves. Take L to be defined by linear equations H 1,H 2,...,H dimv.

14 Trace The trace of a set of points S is the coordinate-wise average. Let L t denote a family of hyperplanes defined by H + t = 0. Let X be an irreducible component of V C n. Suppose S X L t. [Trace test] The trace of S is affine linear in t if and only if S = X L t. Numerical irreducible decomposition using projections from points on the components by A.J. Sommese, J. Verschelde, and C.W. Wampler. [Draw to illustrate affine linear in t] Not limited to curves. Take L to be defined by linear equations H 1,H 2,...,H dimv.

15 Trace The trace of a set of points S is the coordinate-wise average. Let L t denote a family of hyperplanes defined by H + t = 0. Let X be an irreducible component of V C n. Suppose S X L t. [Trace test] The trace of S is affine linear in t if and only if S = X L t. Numerical irreducible decomposition using projections from points on the components by A.J. Sommese, J. Verschelde, and C.W. Wampler. [Draw to illustrate affine linear in t] Not limited to curves. Take L to be defined by linear equations H 1,H 2,...,H dimv.

16 Component membership test Intersect a curve V with a hyperplane L with the intersection sorted by component. Consider a curve V C n with irreducible component X. Component membership test: Let M denote a general hyperplane through B and suppose we have L X (This can be obtained by monodromy if given a point). Deform L M to determine M X from L X. Check if B M X. If not then B is not in X by definition of degree. [Draw]

17 Component membership test Intersect a curve V with a hyperplane L with the intersection sorted by component. Consider a curve V C n with irreducible component X. Component membership test: Let M denote a general hyperplane through B and suppose we have L X (This can be obtained by monodromy if given a point). Deform L M to determine M X from L X. Check if B M X. If not then B is not in X by definition of degree. [Draw]

18 Component membership test Intersect a curve V with a hyperplane L with the intersection sorted by component. Consider a curve V C n with irreducible component X. Component membership test: Let M denote a general hyperplane through B and suppose we have L X (This can be obtained by monodromy if given a point). Deform L M to determine M X from L X. Check if B M X. If not then B is not in X by definition of degree. [Draw]

19 Witness sets We use witness sets to describe algebraic varieties to a computer. A witness set for a d dimensional algebraic variety X in C n is a triple of information: 1. Equations F = 0 that define a variety containing X as an irreducible component. 2. Linear equations H 1 = 0,...,H d = 0 defining a general codimension d linear space L. 3. Witness points: Numerical approximations to the points in X L. Advantages: Sample points. Test membership. Refine approximations. Computational Savings (two line summary): Monodromy homotopy for partial information Decomposition for focus.

20 Systems with groups of indeterminants Many systems define varieties naturally in C n 1 C n 2 C n k. Our results: develop a trace test for these varieties. Euclidean distance degree C n x C n u. Method of moments C n µ,σ C r a C n m. Found 248, 400 solutions with monodromy (special Galois group). Alt s problem in kinematics C 1 C 12. Tensor completion: 21,288,960 solutions. Likelihood equations: C n p C n u. Optimization: primal variables and Lagrange multipliers C n x C m λ.

21 How do we find L V? Regeneration is an equation by equation technique for solving systems of equations. Consider F := x 2 1 x 2 H := 2x 1 + 3x F = 0 defines a curve V. H defines L. We begin with linear algebra and regenerate to nonlinear systems. [Draw.]

22 How do we find L V? Regeneration is an equation by equation technique for solving systems of equations. Consider F := x 2 1 x 2 H := 2x 1 + 3x F = 0 defines a curve V. H defines L. We begin with linear algebra and regenerate to nonlinear systems. [Draw.]

23 How do we find L V? Regeneration is an equation by equation technique for solving systems of equations. Consider F := x 2 1 x 2 H := 2x 1 + 3x F = 0 defines a curve V. H defines L. We begin with linear algebra and regenerate to nonlinear systems. [Draw.]

24 Our results Multiprojective witness sets and a trace test We introduce witness sets for multiprojective varieties A trace test for multiprojective varieties. A membership test for multiprojective varieties Trace test Twelve pages! We have a dimension reduction for C n C m to C 1 C 1 Contact: Jose Israel Rodriguez JoIsRo@Uchicago.edu

Gift Wrapping for Pretropisms

Gift Wrapping for Pretropisms Gift Wrapping for Pretropisms Jan Verschelde University of Illinois at Chicago Department of Mathematics, Statistics, and Computer Science http://www.math.uic.edu/ jan jan@math.uic.edu Graduate Computational

More information

Mechanism and Robot Kinematics, Part II: Numerical Algebraic Geometry

Mechanism and Robot Kinematics, Part II: Numerical Algebraic Geometry Mechanism and Robot Kinematics, Part II: Numerical Algebraic Geometry Charles Wampler General Motors R&D Center Including joint work with Andrew Sommese, University of Notre Dame Jan Verschelde, Univ.

More information

Algorithms to Compute Chern-Schwartz-Macpherson and Segre Classes and the Euler Characteristic

Algorithms to Compute Chern-Schwartz-Macpherson and Segre Classes and the Euler Characteristic Algorithms to Compute Chern-Schwartz-Macpherson and Segre Classes and the Euler Characteristic Martin Helmer University of Western Ontario London, Canada mhelmer2@uwo.ca July 14, 2014 Overview Let V be

More information

Algorithms to Compute Chern-Schwartz-Macpherson and Segre Classes and the Euler Characteristic

Algorithms to Compute Chern-Schwartz-Macpherson and Segre Classes and the Euler Characteristic Algorithms to Compute Chern-Schwartz-Macpherson and Segre Classes and the Euler Characteristic Martin Helmer University of Western Ontario mhelmer2@uwo.ca Abstract Let V be a closed subscheme of a projective

More information

Finding All Real Points of a Complex Algebraic Curve

Finding All Real Points of a Complex Algebraic Curve Finding All Real Points of a Complex Algebraic Curve Charles Wampler General Motors R&D Center In collaboration with Ye Lu (MIT), Daniel Bates (IMA), & Andrew Sommese (University of Notre Dame) Outline

More information

Using Monodromy to Avoid High Precision

Using Monodromy to Avoid High Precision Using Monodromy to Avoid High Precision Daniel J. Bates and Matthew Niemerg Abstract. When solving polynomial systems with homotopy continuation, the fundamental numerical linear algebra computations become

More information

Software for numerical algebraic geometry: a paradigm and progress towards its implementation

Software for numerical algebraic geometry: a paradigm and progress towards its implementation Software for numerical algebraic geometry: a paradigm and progress towards its implementation Daniel J. Bates Jonathan D. Hauenstein Andrew J. Sommese Charles W. Wampler II February 16, 2007 Abstract Though

More information

Solving Polynomial Systems with PHCpack and phcpy

Solving Polynomial Systems with PHCpack and phcpy Solving Polynomial Systems with PHCpack and phcpy Jan Verschelde University of Illinois at Chicago Department of Mathematics, Statistics, and Computer Science http://www.math.uic.edu/ jan jan@math.uic.edu

More information

Planes Intersecting Cones: Static Hypertext Version

Planes Intersecting Cones: Static Hypertext Version Page 1 of 12 Planes Intersecting Cones: Static Hypertext Version On this page, we develop some of the details of the plane-slicing-cone picture discussed in the introduction. The relationship between the

More information

Conic Duality. yyye

Conic Duality.  yyye Conic Linear Optimization and Appl. MS&E314 Lecture Note #02 1 Conic Duality Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/

More information

MATH 19520/51 Class 10

MATH 19520/51 Class 10 MATH 19520/51 Class 10 Minh-Tam Trinh University of Chicago 2017-10-16 1 Method of Lagrange multipliers. 2 Examples of Lagrange multipliers. The Problem The ingredients: 1 A set of parameters, say x 1,...,

More information

Cell decomposition of almost smooth real algebraic surfaces

Cell decomposition of almost smooth real algebraic surfaces Cell decomposition of almost smooth real algebraic surfaces Gian Mario Besana Sandra Di Rocco Jonathan D. Hauenstein Andrew J. Sommese Charles W. Wampler April 16, 2012 Abstract Let Z be a two dimensional

More information

HEURISTIC PATH CHOICE TO AVOID ILL-CONDITIONING IN HOMOTOPY CONTINUATION

HEURISTIC PATH CHOICE TO AVOID ILL-CONDITIONING IN HOMOTOPY CONTINUATION HEURISTIC PATH CHOICE TO AVOID ILL-CONDITIONING IN HOMOTOPY CONTINUATION DANIEL J. BATES 1 AND TIMOTHY E. HODGES 1 Abstract. Homotopy continuation is a numerical method rooted in numerical linear algebra.

More information

Homotopy type of the complement. complex lines arrangements

Homotopy type of the complement. complex lines arrangements On the homotopy type of the complement of complex lines arrangements Department of Geometry-Topology Institute of Mathematics, VAST, Hanoi Singapore December 15, 2008 Introduction Let A be an l-arrangement,

More information

Spectral Methods for Network Community Detection and Graph Partitioning

Spectral Methods for Network Community Detection and Graph Partitioning Spectral Methods for Network Community Detection and Graph Partitioning M. E. J. Newman Department of Physics, University of Michigan Presenters: Yunqi Guo Xueyin Yu Yuanqi Li 1 Outline: Community Detection

More information

Algebraic Geometry of Segmentation and Tracking

Algebraic Geometry of Segmentation and Tracking Ma191b Winter 2017 Geometry of Neuroscience Geometry of lines in 3-space and Segmentation and Tracking This lecture is based on the papers: Reference: Marco Pellegrini, Ray shooting and lines in space.

More information

Computing all Space Curve Solutions of Polynomial Systems by Polyhedral Methods

Computing all Space Curve Solutions of Polynomial Systems by Polyhedral Methods Computing all Space Curve Solutions of Polynomial Systems by Polyhedral Methods Nathan Bliss Jan Verschelde University of Illinois at Chicago Department of Mathematics, Statistics, and Computer Science

More information

A Numerical Method for Computing Border Curves of Bi-parametric Real Polynomial Systems and Applications

A Numerical Method for Computing Border Curves of Bi-parametric Real Polynomial Systems and Applications A Numerical Method for Computing Border Curves of Bi-parametric Real Polynomial Systems and Applications Changbo Chen and Wenyuan Wu Chongqing Key Laboratory of Automated Reasoning and Cognition, Chongqing

More information

Solving Systems of Spline Equations: A Linear Programming-based Approach

Solving Systems of Spline Equations: A Linear Programming-based Approach Engineering, Test & Technology Boeing Research & Technology Solving Systems of Spline Equations: A Linear Programming-based Approach Thomas Grandine Senior Technical Fellow Support and Analytics Technology

More information

CSAP Achievement Levels Mathematics Grade 7 March, 2006

CSAP Achievement Levels Mathematics Grade 7 March, 2006 Advanced Performance Level 4 (Score range: 614 to 860) Students apply equivalent representations of decimals, factions, percents; apply congruency to multiple polygons, use fractional parts of geometric

More information

Lecture 0: Reivew of some basic material

Lecture 0: Reivew of some basic material Lecture 0: Reivew of some basic material September 12, 2018 1 Background material on the homotopy category We begin with the topological category TOP, whose objects are topological spaces and whose morphisms

More information

6.837 LECTURE 7. Lecture 7 Outline Fall '01. Lecture Fall '01

6.837 LECTURE 7. Lecture 7 Outline Fall '01. Lecture Fall '01 6.837 LECTURE 7 1. Geometric Image Transformations 2. Two-Dimensional Geometric Transforms 3. Translations 4. Groups and Composition 5. Rotations 6. Euclidean Transforms 7. Problems with this Form 8. Choose

More information

Quadric Surfaces. Six basic types of quadric surfaces: ellipsoid. cone. elliptic paraboloid. hyperboloid of one sheet. hyperboloid of two sheets

Quadric Surfaces. Six basic types of quadric surfaces: ellipsoid. cone. elliptic paraboloid. hyperboloid of one sheet. hyperboloid of two sheets Quadric Surfaces Six basic types of quadric surfaces: ellipsoid cone elliptic paraboloid hyperboloid of one sheet hyperboloid of two sheets hyperbolic paraboloid (A) (B) (C) (D) (E) (F) 1. For each surface,

More information

Introduction to Multi-body Dynamics

Introduction to Multi-body Dynamics division Graduate Course ME 244) Tentative Draft Syllabus 1. Basic concepts in 3-D rigid-body mechanics 1. Rigid body vs flexible body 2. Spatial kinematics (3-D rotation transformations) and Euler theorem

More information

Constrained Optimization and Lagrange Multipliers

Constrained Optimization and Lagrange Multipliers Constrained Optimization and Lagrange Multipliers MATH 311, Calculus III J. Robert Buchanan Department of Mathematics Fall 2011 Constrained Optimization In the previous section we found the local or absolute

More information

Tropical Implicitization

Tropical Implicitization Tropical Implicitization Jan Verschelde University of Illinois at Chicago Department of Mathematics Statistics and Computer Science http://www.math.uic.edu/ jan jan@math.uic.edu Graduate Computational

More information

Least-Squares Minimization Under Constraints EPFL Technical Report #

Least-Squares Minimization Under Constraints EPFL Technical Report # Least-Squares Minimization Under Constraints EPFL Technical Report # 150790 P. Fua A. Varol R. Urtasun M. Salzmann IC-CVLab, EPFL IC-CVLab, EPFL TTI Chicago TTI Chicago Unconstrained Least-Squares minimization

More information

Lecture 5: Simplicial Complex

Lecture 5: Simplicial Complex Lecture 5: Simplicial Complex 2-Manifolds, Simplex and Simplicial Complex Scribed by: Lei Wang First part of this lecture finishes 2-Manifolds. Rest part of this lecture talks about simplicial complex.

More information

On the classification of real algebraic curves and surfaces

On the classification of real algebraic curves and surfaces On the classification of real algebraic curves and surfaces Ragni Piene Centre of Mathematics for Applications and Department of Mathematics, University of Oslo COMPASS, Kefermarkt, October 2, 2003 1 Background

More information

Planning in Mobile Robotics

Planning in Mobile Robotics Planning in Mobile Robotics Part I. Miroslav Kulich Intelligent and Mobile Robotics Group Gerstner Laboratory for Intelligent Decision Making and Control Czech Technical University in Prague Tuesday 26/07/2011

More information

Investigating Mixed-Integer Hulls using a MIP-Solver

Investigating Mixed-Integer Hulls using a MIP-Solver Investigating Mixed-Integer Hulls using a MIP-Solver Matthias Walter Otto-von-Guericke Universität Magdeburg Joint work with Volker Kaibel (OvGU) Aussois Combinatorial Optimization Workshop 2015 Outline

More information

1 Affine and Projective Coordinate Notation

1 Affine and Projective Coordinate Notation CS348a: Computer Graphics Handout #9 Geometric Modeling Original Handout #9 Stanford University Tuesday, 3 November 992 Original Lecture #2: 6 October 992 Topics: Coordinates and Transformations Scribe:

More information

COMPUTER AIDED GEOMETRIC DESIGN. Thomas W. Sederberg

COMPUTER AIDED GEOMETRIC DESIGN. Thomas W. Sederberg COMPUTER AIDED GEOMETRIC DESIGN Thomas W. Sederberg January 31, 2011 ii T. W. Sederberg iii Preface This semester is the 24 th time I have taught a course at Brigham Young University titled, Computer Aided

More information

Gaussian quadrature for splines via homotopy continuation: rules for C 2 cubic splines

Gaussian quadrature for splines via homotopy continuation: rules for C 2 cubic splines Gaussian quadrature for splines via homotopy continuation: rules for C 2 cubic splines Michael Bartoň a,, Victor Manuel Calo a,b a Numerical Porous Media Center, King Abdullah University of Science and

More information

Lecture 5 2D Transformation

Lecture 5 2D Transformation Lecture 5 2D Transformation What is a transformation? In computer graphics an object can be transformed according to position, orientation and size. Exactly what it says - an operation that transforms

More information

RATIONAL CURVES ON SMOOTH CUBIC HYPERSURFACES. Contents 1. Introduction 1 2. The proof of Theorem References 9

RATIONAL CURVES ON SMOOTH CUBIC HYPERSURFACES. Contents 1. Introduction 1 2. The proof of Theorem References 9 RATIONAL CURVES ON SMOOTH CUBIC HYPERSURFACES IZZET COSKUN AND JASON STARR Abstract. We prove that the space of rational curves of a fixed degree on any smooth cubic hypersurface of dimension at least

More information

phcpy: an API for PHCpack

phcpy: an API for PHCpack phcpy: an API for PHCpack Jan Verschelde University of Illinois at Chicago Department of Mathematics, Statistics, and Computer Science http://www.math.uic.edu/ jan jan@math.uic.edu Graduate Computational

More information

1.1 - Functions, Domain, and Range

1.1 - Functions, Domain, and Range 1.1 - Functions, Domain, and Range Lesson Outline Section 1: Difference between relations and functions Section 2: Use the vertical line test to check if it is a relation or a function Section 3: Domain

More information

Mathematical Tools in Computer Graphics with C# Implementations Table of Contents

Mathematical Tools in Computer Graphics with C# Implementations Table of Contents Mathematical Tools in Computer Graphics with C# Implementations by Hardy Alexandre, Willi-Hans Steeb, World Scientific Publishing Company, Incorporated, 2008 Table of Contents List of Figures Notation

More information

2D Object Definition (1/3)

2D Object Definition (1/3) 2D Object Definition (1/3) Lines and Polylines Lines drawn between ordered points to create more complex forms called polylines Same first and last point make closed polyline or polygon Can intersect itself

More information

2D Transforms. Lecture 4 CISC440/640 Spring Department of Computer and Information Science

2D Transforms. Lecture 4 CISC440/640 Spring Department of Computer and Information Science 2D Transforms Lecture 4 CISC440/640 Spring 2015 Department of Computer and Information Science Where are we going? A preview of assignment #1 part 2: The Ken Burns Effect 2 Where are we going? A preview

More information

Algebra II: Review Exercises

Algebra II: Review Exercises : Review Exercises These notes reflect material from our text, A First Course in Abstract Algebra, Seventh Edition, by John B. Fraleigh, published by Addison-Wesley, 2003. Chapter 7. Advanced Group Theory

More information

Aspects of Geometry. Finite models of the projective plane and coordinates

Aspects of Geometry. Finite models of the projective plane and coordinates Review Sheet There will be an exam on Thursday, February 14. The exam will cover topics up through material from projective geometry through Day 3 of the DIY Hyperbolic geometry packet. Below are some

More information

Constrained Willmore Tori in the 4 Sphere

Constrained Willmore Tori in the 4 Sphere (Technische Universität Berlin) 16 August 2006 London Mathematical Society Durham Symposium Methods of Integrable Systems in Geometry Constrained Willmore Surfaces The Main Result Strategy of Proof Constrained

More information

Software Index. Page numbers in boldface indicate where configuration settings can be found in the users manual portion of this book.

Software Index. Page numbers in boldface indicate where configuration settings can be found in the users manual portion of this book. Software Index Page numbers in boldface indicate where configuration settings can be found in the users manual portion of this book. alphacertified, 226, 227, 248, 328, 332 AMPMaxPrec, 180, 305 AMPSafetyDigits1,

More information

Translations. Geometric Image Transformations. Two-Dimensional Geometric Transforms. Groups and Composition

Translations. Geometric Image Transformations. Two-Dimensional Geometric Transforms. Groups and Composition Geometric Image Transformations Algebraic Groups Euclidean Affine Projective Bovine Translations Translations are a simple family of two-dimensional transforms. Translations were at the heart of our Sprite

More information

Mechanism and Robot Kinematics, Part I: Algebraic Foundations

Mechanism and Robot Kinematics, Part I: Algebraic Foundations Mechanism and Robot Kinematics, Part I: Algebraic Foundations Charles Wampler General Motors R&D Center In collaboration with Andrew Sommese University of Notre Dame Overview Why kinematics is (mostly)

More information

Section 18-1: Graphical Representation of Linear Equations and Functions

Section 18-1: Graphical Representation of Linear Equations and Functions Section 18-1: Graphical Representation of Linear Equations and Functions Prepare a table of solutions and locate the solutions on a coordinate system: f(x) = 2x 5 Learning Outcome 2 Write x + 3 = 5 as

More information

Review for Mastery Using Graphs and Tables to Solve Linear Systems

Review for Mastery Using Graphs and Tables to Solve Linear Systems 3-1 Using Graphs and Tables to Solve Linear Systems A linear system of equations is a set of two or more linear equations. To solve a linear system, find all the ordered pairs (x, y) that make both equations

More information

Special Links. work in progress with Jason Deblois & Henry Wilton. Eric Chesebro. February 9, 2008

Special Links. work in progress with Jason Deblois & Henry Wilton. Eric Chesebro. February 9, 2008 work in progress with Jason Deblois & Henry Wilton February 9, 2008 Thanks for listening! Construction Properties A motivating question Virtually fibered: W. Thurston asked whether every hyperbolic 3-manifold

More information

A primal-dual Dikin affine scaling method for symmetric conic optimization

A primal-dual Dikin affine scaling method for symmetric conic optimization A primal-dual Dikin affine scaling method for symmetric conic optimization Ali Mohammad-Nezhad Tamás Terlaky Department of Industrial and Systems Engineering Lehigh University July 15, 2015 A primal-dual

More information

Sutured Manifold Hierarchies and Finite-Depth Foliations

Sutured Manifold Hierarchies and Finite-Depth Foliations Sutured Manifold Hierarchies and Finite-Depth Christopher Stover Florida State University Topology Seminar November 4, 2014 Outline Preliminaries Depth Sutured Manifolds, Decompositions, and Hierarchies

More information

Data Parallelism and the Support Vector Machine

Data Parallelism and the Support Vector Machine Data Parallelism and the Support Vector Machine Solomon Gibbs he support vector machine is a common algorithm for pattern classification. However, many of the most popular implementations are not suitable

More information

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry 55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography Estimating homography from point correspondence

More information

x 6 + λ 2 x 6 = for the curve y = 1 2 x3 gives f(1, 1 2 ) = λ actually has another solution besides λ = 1 2 = However, the equation λ

x 6 + λ 2 x 6 = for the curve y = 1 2 x3 gives f(1, 1 2 ) = λ actually has another solution besides λ = 1 2 = However, the equation λ Math 0 Prelim I Solutions Spring 010 1. Let f(x, y) = x3 y for (x, y) (0, 0). x 6 + y (4 pts) (a) Show that the cubic curves y = x 3 are level curves of the function f. Solution. Substituting y = x 3 in

More information

Projective spaces and Bézout s theorem

Projective spaces and Bézout s theorem Projective spaces and Bézout s theorem êaû{0 Mijia Lai 5 \ laimijia@sjtu.edu.cn Outline 1. History 2. Projective spaces 3. Conics and cubics 4. Bézout s theorem and the resultant 5. Cayley-Bacharach theorem

More information

Adaptive strategies for solving parameterized systems using homotopy continuation

Adaptive strategies for solving parameterized systems using homotopy continuation Adaptive strategies for solving parameterized systems using homotopy continuation Jonathan D. Hauenstein Margaret H. Regan October 17, 2017 Abstract Three aspects of applying homotopy continuation, which

More information

Polyhedral Homotopies

Polyhedral Homotopies Polyhedral Homotopies Polyhedral homotopies provide proof that mixed volumes count the roots of random coefficient polynomial systems. Mixed-cell configurations store the supports of all start systems

More information

Reducing Points In a Handwritten Curve (Improvement in a Note-taking Tool)

Reducing Points In a Handwritten Curve (Improvement in a Note-taking Tool) Reducing Points In a Handwritten Curve (Improvement in a Note-taking Tool) Kaoru Oka oka@oz.ces.kyutech.ac.jp Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Ryoji

More information

Lecture 2. Topology of Sets in R n. August 27, 2008

Lecture 2. Topology of Sets in R n. August 27, 2008 Lecture 2 Topology of Sets in R n August 27, 2008 Outline Vectors, Matrices, Norms, Convergence Open and Closed Sets Special Sets: Subspace, Affine Set, Cone, Convex Set Special Convex Sets: Hyperplane,

More information

The derivative of a function at one point. 1. Secant lines and tangents. 2. The tangent problem

The derivative of a function at one point. 1. Secant lines and tangents. 2. The tangent problem 1. Secant lines and tangents The derivative of a function at one point A secant line (or just secant ) is a line passing through two points of a curve. As the two points are brought together (or, more

More information

Shadows in Computer Graphics

Shadows in Computer Graphics Shadows in Computer Graphics Steven Janke November 2014 Steven Janke (Seminar) Shadows in Computer Graphics November 2014 1 / 49 Shadows (from Doom) Steven Janke (Seminar) Shadows in Computer Graphics

More information

GEOMETRIC TOOLS FOR COMPUTER GRAPHICS

GEOMETRIC TOOLS FOR COMPUTER GRAPHICS GEOMETRIC TOOLS FOR COMPUTER GRAPHICS PHILIP J. SCHNEIDER DAVID H. EBERLY MORGAN KAUFMANN PUBLISHERS A N I M P R I N T O F E L S E V I E R S C I E N C E A M S T E R D A M B O S T O N L O N D O N N E W

More information

Some algebraic geometry problems arising in the field of mechanism theory. J-P. Merlet INRIA, BP Sophia Antipolis Cedex France

Some algebraic geometry problems arising in the field of mechanism theory. J-P. Merlet INRIA, BP Sophia Antipolis Cedex France Some algebraic geometry problems arising in the field of mechanism theory J-P. Merlet INRIA, BP 93 06902 Sophia Antipolis Cedex France Abstract Mechanism theory has always been a favorite field of study

More information

Curve and Surface Basics

Curve and Surface Basics Curve and Surface Basics Implicit and parametric forms Power basis form Bezier curves Rational Bezier Curves Tensor Product Surfaces ME525x NURBS Curve and Surface Modeling Page 1 Implicit and Parametric

More information

Geometric transformations assign a point to a point, so it is a point valued function of points. Geometric transformation may destroy the equation

Geometric transformations assign a point to a point, so it is a point valued function of points. Geometric transformation may destroy the equation Geometric transformations assign a point to a point, so it is a point valued function of points. Geometric transformation may destroy the equation and the type of an object. Even simple scaling turns a

More information

Simplicial Complexes: Second Lecture

Simplicial Complexes: Second Lecture Simplicial Complexes: Second Lecture 4 Nov, 2010 1 Overview Today we have two main goals: Prove that every continuous map between triangulable spaces can be approximated by a simplicial map. To do this,

More information

Figure 2.1: An example of a convex set and a nonconvex one.

Figure 2.1: An example of a convex set and a nonconvex one. Convex Hulls 2.1 Definitions 2 Convexity is the key to understanding and simplifying geometry, and the convex hull plays a role in geometry akin to the sorted order for a collection of numbers. So what

More information

Web Formalism and the IR limit of 1+1 N=(2,2) QFT. collaboration with Davide Gaiotto & Edward Witten

Web Formalism and the IR limit of 1+1 N=(2,2) QFT. collaboration with Davide Gaiotto & Edward Witten Web Formalism and the IR limit of 1+1 N=(2,2) QFT -or - A short ride with a big machine String-Math, Edmonton, June 12, 2014 Gregory Moore, Rutgers University collaboration with Davide Gaiotto & Edward

More information

Perspective Mappings. Contents

Perspective Mappings. Contents Perspective Mappings David Eberly, Geometric Tools, Redmond WA 98052 https://www.geometrictools.com/ This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy

More information

Computer Graphics and Linear Algebra Rebecca Weber, 2007

Computer Graphics and Linear Algebra Rebecca Weber, 2007 Computer Graphics and Linear Algebra Rebecca Weber, 2007 Vector graphics refers to representing images by mathematical descriptions of geometric objects, rather than by a collection of pixels on the screen

More information

Geometric and Solid Modeling. Problems

Geometric and Solid Modeling. Problems Geometric and Solid Modeling Problems Define a Solid Define Representation Schemes Devise Data Structures Construct Solids Page 1 Mathematical Models Points Curves Surfaces Solids A shape is a set of Points

More information

Lecture 2 - Introduction to Polytopes

Lecture 2 - Introduction to Polytopes Lecture 2 - Introduction to Polytopes Optimization and Approximation - ENS M1 Nicolas Bousquet 1 Reminder of Linear Algebra definitions Let x 1,..., x m be points in R n and λ 1,..., λ m be real numbers.

More information

The Convex Hull of a Space Curve

The Convex Hull of a Space Curve The Convex Hull of a Space Curve Bernd Sturmfels, UC Berkeley (joint work with Kristian Ranestad) The Mathematics of Klee & Grünbaum: 100 Years in Seattle Friday, July 30, 2010 Convex Hull of a Trigonometric

More information

MATRIX REVIEW PROBLEMS: Our matrix test will be on Friday May 23rd. Here are some problems to help you review.

MATRIX REVIEW PROBLEMS: Our matrix test will be on Friday May 23rd. Here are some problems to help you review. MATRIX REVIEW PROBLEMS: Our matrix test will be on Friday May 23rd. Here are some problems to help you review. 1. The intersection of two non-parallel planes is a line. Find the equation of the line. Give

More information

Kernel Methods & Support Vector Machines

Kernel Methods & Support Vector Machines & Support Vector Machines & Support Vector Machines Arvind Visvanathan CSCE 970 Pattern Recognition 1 & Support Vector Machines Question? Draw a single line to separate two classes? 2 & Support Vector

More information

Implicit Matrix Representations of Rational Bézier Curves and Surfaces

Implicit Matrix Representations of Rational Bézier Curves and Surfaces Implicit Matrix Representations of Rational Bézier Curves and Surfaces Laurent Busé INRIA Sophia Antipolis, France Laurent.Buse@inria.fr GD/SPM Conference, Denver, USA November 11, 2013 Overall motivation

More information

Lectures on topology. S. K. Lando

Lectures on topology. S. K. Lando Lectures on topology S. K. Lando Contents 1 Reminder 2 1.1 Topological spaces and continuous mappings.......... 3 1.2 Examples............................. 4 1.3 Properties of topological spaces.................

More information

1. Show that the rectangle of maximum area that has a given perimeter p is a square.

1. Show that the rectangle of maximum area that has a given perimeter p is a square. Constrained Optimization - Examples - 1 Unit #23 : Goals: Lagrange Multipliers To study constrained optimization; that is, the maximizing or minimizing of a function subject to a constraint (or side condition).

More information

EXTREME POINTS AND AFFINE EQUIVALENCE

EXTREME POINTS AND AFFINE EQUIVALENCE EXTREME POINTS AND AFFINE EQUIVALENCE The purpose of this note is to use the notions of extreme points and affine transformations which are studied in the file affine-convex.pdf to prove that certain standard

More information

Machine Learning. Topic 5: Linear Discriminants. Bryan Pardo, EECS 349 Machine Learning, 2013

Machine Learning. Topic 5: Linear Discriminants. Bryan Pardo, EECS 349 Machine Learning, 2013 Machine Learning Topic 5: Linear Discriminants Bryan Pardo, EECS 349 Machine Learning, 2013 Thanks to Mark Cartwright for his extensive contributions to these slides Thanks to Alpaydin, Bishop, and Duda/Hart/Stork

More information

On the undecidability of the tiling problem. Jarkko Kari. Mathematics Department, University of Turku, Finland

On the undecidability of the tiling problem. Jarkko Kari. Mathematics Department, University of Turku, Finland On the undecidability of the tiling problem Jarkko Kari Mathematics Department, University of Turku, Finland Consider the following decision problem, the tiling problem: Given a finite set of tiles (say,

More information

Guidelines for proper use of Plate elements

Guidelines for proper use of Plate elements Guidelines for proper use of Plate elements In structural analysis using finite element method, the analysis model is created by dividing the entire structure into finite elements. This procedure is known

More information

Name: Let the Catmull-Rom curve q(u) be defined by the following control points: p 1 = 0, 1 p 2 = 1, 1 p 3 = 1, 0. p 2. p 1.

Name: Let the Catmull-Rom curve q(u) be defined by the following control points: p 1 = 0, 1 p 2 = 1, 1 p 3 = 1, 0. p 2. p 1. Name: 2 1. Let the Catmull-Rom curve q(u) be defined by the following control points: p 0 = 0, 0 p 1 = 0, 1 p 2 = 1, 1 p 3 = 1, 0 p 4 = 2, 0 y p 1 p 2 p 0 p 3 p 4 x Thus, q(i) =p i for i =1, 2, 3. For

More information

Homogeneous coordinates, lines, screws and twists

Homogeneous coordinates, lines, screws and twists Homogeneous coordinates, lines, screws and twists In lecture 1 of module 2, a brief mention was made of homogeneous coordinates, lines in R 3, screws and twists to describe the general motion of a rigid

More information

Section 1.5. Finding Linear Equations

Section 1.5. Finding Linear Equations Section 1.5 Finding Linear Equations Using Slope and a Point to Find an Equation of a Line Example Find an equation of a line that has slope m = 3 and contains the point (2, 5). Solution Substitute m =

More information

COMPUTATIONAL DYNAMICS

COMPUTATIONAL DYNAMICS COMPUTATIONAL DYNAMICS THIRD EDITION AHMED A. SHABANA Richard and Loan Hill Professor of Engineering University of Illinois at Chicago A John Wiley and Sons, Ltd., Publication COMPUTATIONAL DYNAMICS COMPUTATIONAL

More information

Turning spheres inside-out

Turning spheres inside-out Turning spheres inside-out Mark Grant mark.grant@durham.ac.uk 175 Anniversary Alumni weekend p.1 Smale s paradox Theorem There is a 1-1 correspondence between regular homotopy classes of immersions S 2

More information

Point to Ellipse Distance: A Binary Search Approach

Point to Ellipse Distance: A Binary Search Approach Point to Ellipse Distance: A Binary Search Approach Zhikai Wang http://www.heteroclinic.net Montreal, Quebec, Canada e-mail: wangzhikai@yahoo.com url: Abstract: We give an algorithm to compute the shortest

More information

PERIODS OF ALGEBRAIC VARIETIES

PERIODS OF ALGEBRAIC VARIETIES PERIODS OF ALGEBRAIC VARIETIES OLIVIER DEBARRE Abstract. The periods of a compact complex algebraic manifold X are the integrals of its holomorphic 1-forms over paths. These integrals are in general not

More information

Some Advanced Topics in Linear Programming

Some Advanced Topics in Linear Programming Some Advanced Topics in Linear Programming Matthew J. Saltzman July 2, 995 Connections with Algebra and Geometry In this section, we will explore how some of the ideas in linear programming, duality theory,

More information

Beams. Lesson Objectives:

Beams. Lesson Objectives: Beams Lesson Objectives: 1) Derive the member local stiffness values for two-dimensional beam members. 2) Assemble the local stiffness matrix into global coordinates. 3) Assemble the structural stiffness

More information

Math 259 Winter Unit Test 1 Review Problems Set B

Math 259 Winter Unit Test 1 Review Problems Set B Math 259 Winter 2009 Unit Test 1 Review Problems Set B We have chosen these problems because we think that they are representative of many of the mathematical concepts that we have studied. There is no

More information

Twenty Points in P 3

Twenty Points in P 3 Twenty Points in P 3 arxiv:1212.1841 David Eisenbud Robin Hartshorne Frank-Olaf Schreyer JMM San Diego, 2013 Eisenbud, Hartshorne, Schreyer Twenty Points in P 3 JMM 2013 1 / 16 Outline 1 Linkage and Its

More information

Cam makes a higher kinematic pair with follower. Cam mechanisms are widely used because with them, different types of motion can be possible.

Cam makes a higher kinematic pair with follower. Cam mechanisms are widely used because with them, different types of motion can be possible. CAM MECHANISMS Cam makes a higher kinematic pair with follower. Cam mechanisms are widely used because with them, different types of motion can be possible. Cams can provide unusual and irregular motions

More information

Chapter 9. Linear algebra applications in geometry

Chapter 9. Linear algebra applications in geometry Chapter 9. Linear algebra applications in geometry C.O.S. Sorzano Biomedical Engineering August 25, 2013 9. Linear algebra applications in geometry August 25, 2013 1 / 73 Outline 9 Linear algebra applications

More information

Solutions to Some Examination Problems MATH 300 Monday 25 April 2016

Solutions to Some Examination Problems MATH 300 Monday 25 April 2016 Name: s to Some Examination Problems MATH 300 Monday 25 April 2016 Do each of the following. (a) Let [0, 1] denote the unit interval that is the set of all real numbers r that satisfy the contraints that

More information

Lecture IV Bézier Curves

Lecture IV Bézier Curves Lecture IV Bézier Curves Why Curves? Why Curves? Why Curves? Why Curves? Why Curves? Linear (flat) Curved Easier More pieces Looks ugly Complicated Fewer pieces Looks smooth What is a curve? Intuitively:

More information

Topological Data Analysis - I. Afra Zomorodian Department of Computer Science Dartmouth College

Topological Data Analysis - I. Afra Zomorodian Department of Computer Science Dartmouth College Topological Data Analysis - I Afra Zomorodian Department of Computer Science Dartmouth College September 3, 2007 1 Acquisition Vision: Images (2D) GIS: Terrains (3D) Graphics: Surfaces (3D) Medicine: MRI

More information

Here are some of the more basic curves that we ll need to know how to do as well as limits on the parameter if they are required.

Here are some of the more basic curves that we ll need to know how to do as well as limits on the parameter if they are required. 1 of 10 23/07/2016 05:15 Paul's Online Math Notes Calculus III (Notes) / Line Integrals / Line Integrals - Part I Problems] [Notes] [Practice Problems] [Assignment Calculus III - Notes Line Integrals Part

More information