'Vector product' presentation slideshows

Chapter 12

Vectors. Chapter 12. Objectives. Intro Vectors Representing Vectors Algebra and Geometry of Vectors Cartesian Representation of Vectors in 2-D and 3-D Properties of Vectors in 2-D and 3-D Scalar Product of 2 Vectors Vector Equation of a Line. Scalars and Vectors.

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CE 102 Statics

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Numerical Methods for Sparse Systems

Numerical Methods for Sparse Systems. Ananth Grama Computer Sciences, Purdue University ayg@cs.purdue.edu http://www.cs.purdue.edu/people/ayg. Review of Linear Algebra. A square matrix can be viewed as a graph. Rows (or columns) form vertices. Entries in the matrix correspond to edges.

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Vectors

Vectors. Addition is commutative. ( vi ) If vector u is multiplied by a scalar k , then the product k u is a vector in the same direction as u but k times the magnitude. Vector Directions. Hold your right hand out in front of you as if to shake hands.

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Chapter 3 Rigid Bodies : Equivalent Systems of Forces

Chapter 3 Rigid Bodies : Equivalent Systems of Forces. Introduction. In previous lessons, the bodies were assumed to be particles. . Actually the bodies are a combination of a large number of particles. . body . particle . Introduction.

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VECTOR CALCULUS

VECTOR CALCULUS. multiplication VECTOR. a ? b. b. a. Vector Product. CROSS PRODUCT. ?. Magnitude : Area of the parallelogram generated by a and b. . b. a. Vector Product (contd.). b. a. Vector Product (contd.). ?. Magnitude :. Direction : Perpendicular to

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Conjugate gradients, sparse matrix-vector multiplication, graphs, and meshes

Conjugate gradients, sparse matrix-vector multiplication, graphs, and meshes. Thanks to Aydin Buluc , Umit Catalyurek , Alan Edelman, and Kathy Yelick for some of these slides. T he middleware of scientific computing. Continuous physical modeling. Ax = b. Linear algebra. Computers.

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Iterative Solution Methods

Iterative Solution Methods. Starts with an initial approximation for the solution vector (x 0 ) At each iteration updates the x vector by using the sytem Ax=b During the iterations A, matrix is not changed so sparcity is preserved Each iteration involves a matrix-vector product

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Profiling & Tuning Applications

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Low Rank Approximation and Regression in Input Sparsity Time

Low Rank Approximation and Regression in Input Sparsity Time. David Woodruff IBM Almaden Joint work with Ken Clarkson (IBM Almaden). Talk Outline. Least-Squares Regression Known Results Our Results Low-Rank Approximation Known Results Our Results Experiments. Least-Squares Regression.

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Which came first: Vector Product or Torque?

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Lossy Trapdoor Functions

Lossy Trapdoor Functions. Definition and applications. Lossy Trapdoor Functions. Definition [PW08]. Invertible. Lossy. Lossy Trapdoor Functions. Implications. [BKPW12] What about the IB setting ?. Lossy Trapdoor Functions. Constructing a primitive. Setup. Encrypt. Decrypt. Gen.

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Chapter 15. The Fast Fourier Transform

Wireless & Emerging Networking System Laboratory. Chapter 15. The Fast Fourier Transform. 09 December 2013 Oka Danil Saputra (20136135) IT Convergence Kumoh National Institute of Technology. Fourier Analysis . Represent continuous function by sinusoidal (sine and cosine) functions.

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CDP Tutorial 4 Basics of Parallel Algorithm Design

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Continuum Mechanics

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Experimental Mathematics

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Matrix Multiplication

Matrix Multiplication. The Myth, The Mystery, The Majesty. Matrix Multiplication. Simple, yet important problem Many fast serial implementations exist Atlas Vendor BLAS Natural to want to parallelize. The Problem. Take two matrices, A and B, and multiply them to get a 3 rd matrix C

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Fictitious Force

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