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Click Expand[edit source]

In the first exercise it says "Click Expand for the answers.", yet there is no expand button/link. (The preceding unsigned comment was added by (talkcontribs) 02:49, 15 January 2008)

New Post(Jan 2008)

The section “The basic idea” appears unnecessarily complicated: Scalar is not defined in a simple manner, neither is vector. Neither are particularly complex conceptions at this level. Either the assumption is that the words require no explanation, which is unlikely in view of the next paragraph, or they do require one, in which case, the concept is, as I say below, not difficult.

There is the sentence, “Detailed explanation of vectors may be found at Wikibooks linear algebra.” Would this be necessary were the article to be clearer? Indeed the detailed explanation may not be needed at all until later in the course when a little more groundwork has been covered.)

“A unit vector is a special vector which has magnitude 1 and points along one of the coordinate frame's axes*. This is better illustrated by a diagram.” – no diagram – but the explanation here is clear enough. (*coordinate frame's = graph's)

Simply put, a scalar is a number on the axis of a graph; in 2x + 3y – 8z • the 2, the 3 and the -8 are scalar – they tell you the quantity of the units on the axes. • The x and the y are the axes of the graph and tell you what the thing is (time / size / quantity / quality / etc) and • a vector is the line (usually, but not always, starting at 0) that is resultant of the other lines and ends at the point indicated by the numbers (scalars) on the axes. (see diagrams.) The size and direction of the vector is important and is usually what is being sought

Thus the vector 2x + 3y +4z is the line to the point at which right angled lines from 2 on the x axis, 3 on the y axis and 4 on the z axis intersect. This is the vector 2x + 3y +4z

The final line of this section reads:

“The magnitude of a vector is computed by (root of sum equation). For example, in two-dimensional space, this equation reduces to (root (x squared plus y squared)) .”

This is a masterpiece of obscurity. 1. “The magnitude of a vector is computed by...” I’m sure, should read, “You can find the magnitude of a vector with this formula…” 2. But in the formula, (root of sum) why is it not clear what x and i are? 3. The term, “in two-dimensional space” can be replaced with, “if there are only 2 axes” 4. There is no explanation or even a hint as to why (root of sum) “reduces to” (root (x squared plus y squared)) and 5. the final equation (root (x squared plus y squared)) is no more than Pythagoras’s Theorem, although heavily (and to my mind pointlessly) disguised.

Let me say that the idea of Wikiversity is excellent and I am, despite the above, grateful to and impressed by the person who gave his/her time to this section. He/she clearly knows the subject like the back of their hand; however, many would-be readers (and I am living proof) have not had his/her years to stare at the back of his/her hand.

Perhaps, taken with the observation by the previous poster, this is “a work in progress.” (The preceding unsigned comment was added by (talkcontribs) 13:36, 3 December 2007)

To both of the previous commentators: Thank you very much for your feedback.
Indeed Wikiversity is a work in progress, we are a wiki - this means just be bold to optimize the text. Here you find more info about Wikiversity: Template:Welcome. If you have more questions, you can also visit the Wikiversity:Chat, ----Erkan Yilmaz (Wikiversity:Chat, wiki blog) 07:35, 15 January 2008 (UTC)
“A unit vector is a special vector which has magnitude 1 and points along one of the coordinate frame's axes*. This is better illustrated by a diagram.” – no diagram – but the explanation here is clear enough. (*coordinate frame's = graph's)
It is not just clear, it is completely wrong. There is nothing that tells that all single vectors are collinear to axis. It is nonsense. One may easily have ones that are oriented at any angle wrt the axis. --Javalenok (discusscontribs) 13:10, 11 June 2013 (UTC)

Error in text (not a typo)[edit source]

This is wrong:

If you just look at the figure, you can see that the component of a vector along the basis vector is NOT equal to it's component, . You need to establish a dual space such as the reciprocal lattice basis in solid state physics.

You could also represent the same vector in terms of another set of basis vectors () as shown in Figure 1(b). In that case, the components of the vector are and we can write

Note that the basis vectors and do not necessarily have to be unit vectors. All we need is that they be linearly independent, that is, it should not be possible for us to represent one solely in terms of the others.


Figure 1: A vector and its basis.
After posting this a while back and forgetting to sign, I have decided to replace this with a correct discussion based on Wikipedia.--guyvan52 (discusscontribs) 20:47, 25 May 2014 (UTC)

moved a large section to another page[edit source]

I moved a large part of this page to Vector calculus

This what was moved:

BEGIN TEXT THAT HAS BEEN MOVED TO Vector calculus[edit source]

So far we have dealt with constant vectors. It also helps if the vectors are allowed to vary in space. Then we can define derivatives and integrals and deal with vector fields. Some basic ideas of vector calculus are discussed below.

Derivative of a vector valued function[edit source]

Let be a vector function that can be represented as

where is a scalar.

Then the derivative of with respect to is

Note: In the above equation, the unit vectors (i=1,2,3) are assumed constant.
If and are two vector functions, then from the chain rule we get

Scalar and vector fields[edit source]

Let be the position vector of any point in space. Suppose that there is a scalar function () that assigns a value to each point in space. Then

represents a scalar field. An example of a scalar field is the temperature. See Figure4(a).

Figure 4: Scalar and vector fields.

If there is a vector function () that assigns a vector to each point in space, then

represents a vector field. An example is the displacement field. See Figure 4(b).

Gradient of a scalar field[edit source]

Let be a scalar function. Assume that the partial derivatives of the function are continuous in some region of space. If the point has coordinates () with respect to the basis (), the gradient of is defined as

In index notation,

The gradient is obviously a vector and has a direction. We can think of the gradient at a point being the vector perpendicular to the level contour at that point.

It is often useful to think of the symbol as an operator of the form

Divergence of a vector field[edit source]

If we form a scalar product of a vector field with the operator, we get a scalar quantity called the divergence of the vector field. Thus,

In index notation,

If , then is called a divergence-free field.

The physical significance of the divergence of a vector field is the rate at which some density exits a given region of space. In the absence of the creation or destruction of matter, the density within a region of space can change only by having it flow into or out of the region.

Curl of a vector field[edit source]

The curl of a vector field is a vector defined as

The physical significance of the curl of a vector field is the amount of rotation or angular momentum of the contents of a region of space.

Laplacian of a scalar or vector field[edit source]

The Laplacian of a scalar field is a scalar defined as

The Laplacian of a vector field is a vector defined as

Identities in vector calculus[edit source]

Some frequently used identities from vector calculus are listed below.

Green-Gauss Divergence Theorem[edit source]

Figure 5: Volume for application of the divergence theorem.

Let be a continuous and differentiable vector field on a body with boundary . The divergence theorem states that


where is the outward unit normal to the surface (see Figure 5).

In index notation,

Stokes' theorem[edit source]

Stokes' Theorem.svg

The Kelvin–Stokes theorem relates the surface integral of the curl of a vector field F over a surface Σ in Euclidean three-space to the line integral of the vector field over its boundary ∂Σ:

According to Wikipedia[1], this form of the theorem was first discovered by Lord Kelvin, who communicated it to George Stokes in a letter dated July 2, 1850. Stokes set the theorem as a question on the 1854 Smith's Prize exam, which led to the result bearing his name.



Free and fixed vectors?[edit source]

I'm not sure we need to disinguish between free vectors and fixed vectors. I appreciate motive for making the distinction, but vectors are generally defined to be free.---Guy vandegrift (discusscontribs) 10:27, 6 April 2016 (UTC)

Rework of article[edit source]

This article has only ONE reference (I'm not counting the reference to WIKIPEDIA) and only describes Euclidean vectors (magnitude and direction) rather than abstract vectors. These issues make an abstract definition impossible / unclear for those studying things such as tensors, topology, et cetera. This is a very basic definition of vectors that most pure math applications don't have too much use for. This article has WAY too many unsourced statements and needs to be re-done. It's been over a year since the last edit. I was thinking something similar to my sandbox over at ratwiki: (wikiversity blocks me from adding to my own sandbox so I can't link there...) ZackaryCW (discusscontribs) 22:03, 11 July 2019 (UTC)

The current definitions of vectors do not describe all vectors. It is incomplete and with these definitions is impossible to explain abstract vectors or even basic stuff like bi-vectors is almost impossible with this definition. ZackaryCW (discusscontribs) 11:20, 12 July 2019 (UTC)