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Quiz 2 Solutions 1. Let V be the set of all ordered pairs of real
... A(x1 + x2 ) = b + b = 2b 6= b as b is nonzero. To show it is not closed under scalar multiplication, if x1 is a solution and r is real number, then A(rx1 ) = r(Ax1 ) = rb. Note: You only need to show that it fails one of the two closure properties. Also, another argument that it is not a subspace wo ...
... A(x1 + x2 ) = b + b = 2b 6= b as b is nonzero. To show it is not closed under scalar multiplication, if x1 is a solution and r is real number, then A(rx1 ) = r(Ax1 ) = rb. Note: You only need to show that it fails one of the two closure properties. Also, another argument that it is not a subspace wo ...
Quiz 2 - CMU Math
... Proof. Clearly W is nonempty. Then you may directly prove W is closed under addition and scalar multiplication. But the following method is more convenient. Since ...
... Proof. Clearly W is nonempty. Then you may directly prove W is closed under addition and scalar multiplication. But the following method is more convenient. Since ...
Operations with vectors
... vector by 180o. Two examples ( = -1 and = 2) are given in the picture. Here it is important to check that the scalar multiplication is compatible with vector addition in the following sense: (a + b) = a + b for all vectors a and b and all scalars . One can also show that a - b = a + (-1)b. ...
... vector by 180o. Two examples ( = -1 and = 2) are given in the picture. Here it is important to check that the scalar multiplication is compatible with vector addition in the following sense: (a + b) = a + b for all vectors a and b and all scalars . One can also show that a - b = a + (-1)b. ...
Parametric Equations
... parametric equations of a curve with parameter y g(t) ex. Graph the following parametric equations and find the Cartesian equation of the curve that the equations trace. ...
... parametric equations of a curve with parameter y g(t) ex. Graph the following parametric equations and find the Cartesian equation of the curve that the equations trace. ...
1 Why is a parabola not a vector space
... does not hold, it is enough to provide one pair of vectors that is NOT closed under addition. Similarly, it is enough to provide one counterexample to show that the property ‘closed under scalar multiplication’ does not hold. If the MATH 308L class had 47 girls and 1 boy, the statement “Every studen ...
... does not hold, it is enough to provide one pair of vectors that is NOT closed under addition. Similarly, it is enough to provide one counterexample to show that the property ‘closed under scalar multiplication’ does not hold. If the MATH 308L class had 47 girls and 1 boy, the statement “Every studen ...
Notes from Unit 5
... Now we want to generalize the concept of vector space. In its most general form, we should begin with the scalars we are allowed to multiply by. They could from any system within which you can add, subtract, multiply and (except by 0) divide, and all the usual rules of arithmetic hold. Well, maybe n ...
... Now we want to generalize the concept of vector space. In its most general form, we should begin with the scalars we are allowed to multiply by. They could from any system within which you can add, subtract, multiply and (except by 0) divide, and all the usual rules of arithmetic hold. Well, maybe n ...
Ch 6 PPT (V1)
... Standard Basis • The column vectors in the identity matrix for the standard basis [e1 e2 …en]=In • Remember i, j, k from physics • AX=B has a solution only if each and every column of B is in (A), i.e. – (B) is a subset of (A) • This can be tested by – constructing the matrix [A B]; – computing ...
... Standard Basis • The column vectors in the identity matrix for the standard basis [e1 e2 …en]=In • Remember i, j, k from physics • AX=B has a solution only if each and every column of B is in (A), i.e. – (B) is a subset of (A) • This can be tested by – constructing the matrix [A B]; – computing ...
Vector space
A vector space (also called a linear space) is a collection of objects called vectors, which may be added together and multiplied (""scaled"") by numbers, called scalars in this context. Scalars are often taken to be real numbers, but there are also vector spaces with scalar multiplication by complex numbers, rational numbers, or generally any field. The operations of vector addition and scalar multiplication must satisfy certain requirements, called axioms, listed below. Euclidean vectors are an example of a vector space. They represent physical quantities such as forces: any two forces (of the same type) can be added to yield a third, and the multiplication of a force vector by a real multiplier is another force vector. In the same vein, but in a more geometric sense, vectors representing displacements in the plane or in three-dimensional space also form vector spaces. Vectors in vector spaces do not necessarily have to be arrow-like objects as they appear in the mentioned examples: vectors are regarded as abstract mathematical objects with particular properties, which in some cases can be visualized as arrows.Vector spaces are the subject of linear algebra and are well understood from this point of view since vector spaces are characterized by their dimension, which, roughly speaking, specifies the number of independent directions in the space. A vector space may be endowed with additional structure, such as a norm or inner product. Such spaces arise naturally in mathematical analysis, mainly in the guise of infinite-dimensional function spaces whose vectors are functions. Analytical problems call for the ability to decide whether a sequence of vectors converges to a given vector. This is accomplished by considering vector spaces with additional structure, mostly spaces endowed with a suitable topology, thus allowing the consideration of proximity and continuity issues. These topological vector spaces, in particular Banach spaces and Hilbert spaces, have a richer theory.Historically, the first ideas leading to vector spaces can be traced back as far as the 17th century's analytic geometry, matrices, systems of linear equations, and Euclidean vectors. The modern, more abstract treatment, first formulated by Giuseppe Peano in 1888, encompasses more general objects than Euclidean space, but much of the theory can be seen as an extension of classical geometric ideas like lines, planes and their higher-dimensional analogs.Today, vector spaces are applied throughout mathematics, science and engineering. They are the appropriate linear-algebraic notion to deal with systems of linear equations; offer a framework for Fourier expansion, which is employed in image compression routines; or provide an environment that can be used for solution techniques for partial differential equations. Furthermore, vector spaces furnish an abstract, coordinate-free way of dealing with geometrical and physical objects such as tensors. This in turn allows the examination of local properties of manifolds by linearization techniques. Vector spaces may be generalized in several ways, leading to more advanced notions in geometry and abstract algebra.