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Can the difference of 2 vectors be orthogonal?
The answer will depend on orthogonal to WHAT!
Show some details about the set of all orthogonal matrices?
The set of all orthogonal matrices consists of square matrices ( Q ) that satisfy the condition ( Q^T Q = I ), where ( Q^T ) is the transpose of ( Q ) and ( I ) is the identity matrix. This means that the columns (and rows) of an orthogonal matrix are orthonormal vectors. Orthogonal matrices preserve the Euclidean norm of vectors and the inner product, making them crucial in various applications such as rotations and reflections in geometry. The determinant of an orthogonal matrix is either ( +1 ) or ( -1 ), corresponding to special orthogonal matrices (rotations) and improper orthogonal matrices (reflections), respectively.
What are difference between scalars and vectors?
What are difference between scalars and vectors
What is a vector which is orthogonal to the other vectors and is coplanar with the other vectors called?
In a plane, each vector has only one orthogonal vector (well, two, if you count the negative of one of them). Are you sure you don't mean the normal vector which is orthogonal but outside the plane (in fact, orthogonal to the plane itself)?
How to find orthogonal vector?
Given one vector a, any vector that satisfies a.b=0 is orthogonal to it. That is a set of vectors defining a plane orthogonal to the original vector.The set of vectors defines a plane to which the original vector a is the 'normal'.
Can the difference of 2 vectors be orthogonal?
The answer will depend on orthogonal to WHAT!
Show some details about the set of all orthogonal matrices?
The set of all orthogonal matrices consists of square matrices ( Q ) that satisfy the condition ( Q^T Q = I ), where ( Q^T ) is the transpose of ( Q ) and ( I ) is the identity matrix. This means that the columns (and rows) of an orthogonal matrix are orthonormal vectors. Orthogonal matrices preserve the Euclidean norm of vectors and the inner product, making them crucial in various applications such as rotations and reflections in geometry. The determinant of an orthogonal matrix is either ( +1 ) or ( -1 ), corresponding to special orthogonal matrices (rotations) and improper orthogonal matrices (reflections), respectively.
What does it mean when the dot product between two vectors is zero?
When the dot product between two vectors is zero, it means that the vectors are perpendicular or orthogonal to each other.
What are difference between scalars and vectors?
What are difference between scalars and vectors
What is the term givin to vectors the go in different directions?
Vectors that go in different directions are called orthogonal vectors. This means that the vectors are perpendicular to each other, with a 90 degree angle between them.
What is a vector which is orthogonal to the other vectors and is coplanar with the other vectors called?
In a plane, each vector has only one orthogonal vector (well, two, if you count the negative of one of them). Are you sure you don't mean the normal vector which is orthogonal but outside the plane (in fact, orthogonal to the plane itself)?
How to find orthogonal vector?
Given one vector a, any vector that satisfies a.b=0 is orthogonal to it. That is a set of vectors defining a plane orthogonal to the original vector.The set of vectors defines a plane to which the original vector a is the 'normal'.
When are vectors orthogonal?
When they are at right angles to one another.
Why is the difference between scalars and vectors important?
Without the difference between scalars and vectors the Universe doesn't work !
A vedtor which is perpendicular to every vector?
The zero vector is not perpendicular to all vectors, but it is orthogonal to all vectors.
Difference between orthogonal and perpendicular lines?
Orthogonal and perpendicular are essentially the same thing: When two lines, planes, etc. intersect at a right angle, or 90 degrees, they are orthogonal/perpendicular.Orthogonal is simply a term used more commonly for vectors, when they have a scalar/inner/dot product of 0, as:vector u X vector v = (length of vector u) X (length of vector v) X cos @ ,@ being the angle between the two vectors.When the scalar product is 0, that is because @ is 90 degrees, and cos 90 = 0. Therefore, the vectors u and v are orthogonal.
Show that only N orthogonal vectors can be formed from N linearly independent vectors?
shut up now
