Q: What is orthogonal trajectories?

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The answer will depend on orthogonal to WHAT!

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)?

yes. not sure of the proof though.

All vectors that are perpendicular (their dot product is zero) are orthogonal vectors.Orthonormal vectors are orthogonal unit vectors. Vectors are only orthonormal if they are both perpendicular have have a length of 1.

statistically independent

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a family of curves whose family of orthogonal trajectories is the same as the given family, is called self orthogonal trajectories.

Self orthogonal trajectories are a family of curves whose family of orthogonal trajectories is the same as the given family. This is a term that is not very widely used.

we dont ever

orthogonal trajectories represent the curves in which the magnitude of the velocity or the force is the same at each point on that curve. In the case of the flow field the orthognal trajectories are called the velocity potential and in the case of Force Fileds the orthogonal trajectories are called equipotential curves--curves in which the magnitude of the Force is the same.

Orthogonal signal space is defined as the set of orthogonal functions, which are complete. In orthogonal vector space any vector can be represented by orthogonal vectors provided they are complete.Thus, in similar manner any signal can be represented by a set of orthogonal functions which are complete.

The answer will depend on orthogonal to WHAT!

it is planning of orthogonal planning

it is planning of orthogonal planning

Orthogonal - novel - was created in 2011.

Orthogonal is a term referring to something containing right angles. An example sentence would be: That big rectangle is orthogonal.

Richard Askey has written: 'Three notes on orthogonal polynomials' -- subject(s): Orthogonal polynomials 'Recurrence relations, continued fractions, and orthogonal polynomials' -- subject(s): Continued fractions, Distribution (Probability theory), Orthogonal polynomials 'Orthogonal polynomials and special functions' -- subject(s): Orthogonal polynomials, Special Functions

A matrix A is orthogonal if itstranspose is equal to it inverse. So AT is the transpose of A and A-1 is the inverse. We have AT=A-1 So we have : AAT= I, the identity matrix Since it is MUCH easier to find a transpose than an inverse, these matrices are easy to compute with. Furthermore, rotation matrices are orthogonal. The inverse of an orthogonal matrix is also orthogonal which can be easily proved directly from the definition.