All vectors and subspaces are in R". Check the true statements below: DA. The orthogonal projection ŷ of y onto a subspace W can somtimes depend on the orthogonal basis for W used to compute ĝŷ. OB. If y is in a subspace W, then the orthogonal projection of y onto W is y itself. C. For each y and each subspace W, the vector y – projw (y) is orthogonal to W. D. If the columns of an n x p matrix U are orthonormal, then UUTY is the orthogonal projection of y onto the column space of U. JE. If z is orthogonal to uj and uz and if W = Span{u1, u2}, then z must be in W+.

All vectors and subspaces are in R". Check the true statements below: DA. The orthogonal projection ŷ of y onto a subspace W can somtimes depend on the orthogonal basis for W used to compute ĝŷ. OB. If y is in a subspace W, then the orthogonal projection of y onto W is y itself. C. For each y and each subspace W, the vector y – projw (y) is orthogonal to W. D. If the columns of an n x p matrix U are orthonormal, then UUTY is the orthogonal projection of y onto the column space of U. JE. If z is orthogonal to uj and uz and if W = Span{u1, u2}, then z must be in W+.

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter2: Systems Of Linear Equations

Section2.3: Spanning Sets And Linear Independence

Problem 22EQ

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