cs140-13-stencilCGmatvecgraph
... Conjugate gradient in general • CG can be used to solve any system Ax = b, if … • The matrix A is symmetric (aij = aji) … • … and positive definite (all eigenvalues > 0). • Symmetric positive definite matrices occur a lot in scientific computing & data analysis! • But usually the matrix isn’t just ...
... Conjugate gradient in general • CG can be used to solve any system Ax = b, if … • The matrix A is symmetric (aij = aji) … • … and positive definite (all eigenvalues > 0). • Symmetric positive definite matrices occur a lot in scientific computing & data analysis! • But usually the matrix isn’t just ...
MAT 240 - Problem Set 3 Due Thursday, October 9th Questions 3a
... b) Assume that F has the property that 1 + 1 6= 0. Let f (x) ∈ V be a nonzero function such that f (−c) = f (c) for all c ∈ F , and let g(x) ∈ V be a nonzero function such that g(−c) = −g(c) for all c ∈ F . Prove that { f (x), g(x) } is linearly independent. 9. Suppose that x, y and z are distinct v ...
... b) Assume that F has the property that 1 + 1 6= 0. Let f (x) ∈ V be a nonzero function such that f (−c) = f (c) for all c ∈ F , and let g(x) ∈ V be a nonzero function such that g(−c) = −g(c) for all c ∈ F . Prove that { f (x), g(x) } is linearly independent. 9. Suppose that x, y and z are distinct v ...
Test I
... 3. (15 pts.) Determine whether the vectors a1 = (−1 − 2 1 2)T , a2 = (2 1 1 0)T , a3 = (3 3 0 − 2)T are linearly independent (LI) or linearly dependent (LD). If they are LD, find at least one nonzero linear combination of them that is 0. 4. A linear system Ax = b has the augmented matrix [A|b] given ...
... 3. (15 pts.) Determine whether the vectors a1 = (−1 − 2 1 2)T , a2 = (2 1 1 0)T , a3 = (3 3 0 − 2)T are linearly independent (LI) or linearly dependent (LD). If they are LD, find at least one nonzero linear combination of them that is 0. 4. A linear system Ax = b has the augmented matrix [A|b] given ...
Cartesian tensor
In geometry and linear algebra, a Cartesian tensor uses an orthonormal basis to represent a tensor in a Euclidean space in the form of components. Converting a tensor's components from one such basis to another is through an orthogonal transformation.The most familiar coordinate systems are the two-dimensional and three-dimensional Cartesian coordinate systems. Cartesian tensors may be used with any Euclidean space, or more technically, any finite-dimensional vector space over the field of real numbers that has an inner product.Use of Cartesian tensors occurs in physics and engineering, such as with the Cauchy stress tensor and the moment of inertia tensor in rigid body dynamics. Sometimes general curvilinear coordinates are convenient, as in high-deformation continuum mechanics, or even necessary, as in general relativity. While orthonormal bases may be found for some such coordinate systems (e.g. tangent to spherical coordinates), Cartesian tensors may provide considerable simplification for applications in which rotations of rectilinear coordinate axes suffice. The transformation is a passive transformation, since the coordinates are changed and not the physical system.