You must log in or register to comment.
It’s a dynamically-sized list of objects of the same type stored contiguously in memory.
It’s like a fancy list.
So is a wedding gift registry.
No, this is Patrick!
It’s a dynamically-sized list of objects of the same type stored contiguously in memory.
dynamically-sized: The size of it can change as needed.
list: It stores multiple things together.
object: A bit of programmer defined data.
of the same type: all the objects in the list are defined the same way
stored contigiously in memory: if you think of memory as a bookshelf then all the objects on the list would be stored right next to each other on the bookshelf rather than spread across the bookshelf.
Dynamically sized but stored contiguously makes the systems performance engineer in me weep. If the lists get big, the kernel is going to do so much churn.
Contiguous storage is very fast in terms of iteration though often offsetting the cost of allocation
Modern CPUs are also extremely efficient at dealing with contiguous data structures. Branch prediction and caching get to shine on them.
Avoiding memory access or helping CPU access it all upfront switches physical domain of computation.
Which is why you should:
- Preallocate the vector if you can guesstimate the size
- Use a vector library that won’t reallocate the entire vector on every single addition (like Rust, whose
Vecdoubles in size every time it runs out of space)
Memory is fairly cheap. Allocation time not so much.
matlab likes to pick the smallest available spot in memory to store a list, so for loops that increase the size of a matrix it’s recommended to preallocate the space using a matrix full of zeros!
Is that churn or chum? (RN or M)
Churm
Many things like each other lined up in a row, and you can take some away or put more in.
No, it’s an n-tuple with certain algebraic properties.
This is such an understated but useful description in this context. It’s also how I understood algebra for applied matrix computation.
I was just coming down from THC when I wrote this, so I’m extra jazzed you liked it. 😁
Edit: also, love the username.
Its the algebraic properties that are important, not all vectors are n-tuples, eg the set of polynomials of degree less than n.
You need a basis to coordinate a vector, you can work with vectors without doing that and just deal with the algebraic properties. The coordinate representation is dependent on the basis chosen and isn’t fundamental to the vector. So calling them n-tuples isn’t technically correct.
You can turn them into a set of coordinates if you have a basis, but the fact that you can do that is because of the algebraic properties so it’s those properties which define what a vector is.
I think a better example to show how vectors don’t necessarily need to be what people conceptualize as n-tuples would have been the real numbers. (Of course, these can be considered 1-tuples, but the same can be said of any arbitrary set element that is not itself a tuple with more entries.) A cooler example would have been R[x] (the ring of real-valued polynomials of a single variable) especially since an isomorphic ring using n-tuples would be a more cumbersome representation of the algebra.
So an ArrayList?
No. ArrayList is thread safe and implements the collections API. Vector doesn’t. Though if you’re using Java, there’s almost no instance where you would want to use a Vector instead of ArrayList.
ArrayList isn’t thread-safe, though…
Thread safe as in it raises an exception instead of breaking your list.
Only if one thread modifies it while another one is iterating over it, if two threads try to modify the list at once there isn’t any kind of synchronization and it really could break your list.
For everything else, there’s
Collections.synchronizedList(new ArrayList<>())
What do you mean? A vector is a direction and magnitude!
Maybe they mean std::vector in C++?
It’s a terrible name. The math answer is what I would give.
It’s not a terrible name, since it’s derived from the mathematical construct of vectors as
n-tuples. In the case of vectors in programming,nrelates to the size of the underlying array, and the tuple consists of the elements of the vector.That makes sense.
I myself was confused, when I first saw what a vector did in practice.
Really bad name.But then I didn’t take Comp Sci.
The only correct answer for a 101 introduction. It’s an incredible powerful intuition even in contexts where vectors are seemingly used as a list of numbers.
You can also define a vector by the equivalent “sides of the right triangle”. In 2D, the x,y coordinates. In computer science, vectors are n-tuples, so they represent a math/physics vector but in n-dimensions.
Yes, and as linear algebra teaches, to convert a vector from direction and magnitude to a list of numbers (components), follow these steps:
- Let the magnitude of the vector be represented by the symbol |A| or A.
- Let the direction of the vector be represented by the angle θ, which is measured counterclockwise from the positive x-axis.
- The x-component of the vector is given by: Ax = |A| cos(θ)
- The y-component of the vector is given by: Ay = |A| sin(θ)
The vector can now be represented as a list of numbers: A = (Ax, Ay)
For example, if a vector has a magnitude of 5 units and a direction of 30° counterclockwise from the positive x-axis, its components would be:
Ax = 5 cos(30°) ≈ 4.33 units Ay = 5 sin(30°) ≈ 2.50 units
The vector can now be written as A = (4.33, 2.50)
Did nobody else’s CS department require a bunch of linear algebra courses? A vector is an element of vector space.
Sets are just objects in the category of Set.
That is quite possibly the least helpful answer you could give.
Also the most correct :)
Q: what is a vector? A: it is a vector
My university requires two for the CS program, as I recall.
Ooh, do tensors next!
You should ask your biologist friend and your physicist friend and your compsci friend to debate about what vectors are. Singularities, too.
Singularities, too.
/dev/null
Tensors are easy!
It’s just a fancy list of fancy lists! :D
you just need to
carandcdryourconscell …Well mathematically isn’t it an n by 1 matrix.
Not always. Any m by n matrix is also a vector. Polynomials are vectors. As are continuous functions.
A vector is an element of a vector space over a field. These are sets which have a few operations, vector addition and scalar multiplication, and obey some well known rules, such as the existence of a zero vector (identity for vector addition), associativity and commutativity of vector addition, distributivity of scalar multiplication over vector sums, that sort of thing!
These basic properties give rise to more elaborate concepts such as linear independence, spanning sets, and the idea of a basis, though not all vector spaces have a finite basis.
How are polynomials vectors how does that work?
Say u have polynomial f(x)= a + bx + cx^2 + dx^3
How is that represented as a vector? Or is it just one of those maths well technically things? Cos as far as I’m aware √g = π = e = 3.
Are differential eqs also vectors?
Your polynomial, f(x) = a + bx +cx^2 + dx^3, is an element of the vector space P3®, the polynomial vector space of degree at most 3 over the reals. This space is isomorphic to R^4 and it has a standard basis: {1, x, x^2, x^3}. Then you can see that any such f(x) may be written as a linear combination of the basis vectors with real valued scalars.
As an exercise, you can check that P3® satisfies some of the properties of vector spaces yourself (existence of zero vector, associativity and commutativity of vector addition, distributivity of scalar multiplication over vector sums).
What happens to elements with powers of x above 3? Say we multiply the example vector above with itself. We would end up with a component d2x6, witch is not part of the P3R vector space, right?
Do we need a special multiplication rule to handle powers of x above 3? I’ve worked with quaternions before, which has " special" multiplication rules by defining i j and k.
Multiplication of two vectors is not an operation defined on vector spaces. If you want that, you’re looking at either a structure known as an inner product space or an algebra over a field.
Note that the usual notion of polynomial multiplication doesn’t apply to polynomial vector spaces, nor does it agree with the definition of an inner product nor the bilinear product of an algebra.
That’s only if you’re working with the perspective of it being a polynomial. When you’re considering the polynomial as a vector however, that operation simply doesn’t exist
Wouldn’t N by M be a tensor? Magnitude and direction only need one entry per DOF.
Every vector is a tensor. Matrices are vectors because m by n matrices form vector spaces. Magnitude and direction have nothing to do with the definition of vectors which are just elements of vector spaces.
All vectors are tensors but not vice versa. And every page/definition of vector I’ve seen references magnitude and direction, even the vector space page you linked.
It looks like “vector” commonly refers to geometric vectors which is what most folks in this thread are discussing.
Would N by M vectors be imaginary, where each DOF has real and imaginary components?
Continuous functions on [0,1] are vectors. Magnitude and direction are meaningless in that vector space, usually called C[0,1]. Magnitude and direction are not fundamental properties of vectors.
n by m matrices (and the vector spaces to which they belong) are perhaps best thought of similarly to functions and function spaces. Not as geometric objects, but as linear transformations (which they are).
It’s an array.
First time I heard of vectors in comp-sci was in C++. The naming still doesn’t make sense to me.
I know what a vector is in computer science; do you know why it’s named that?
I asked my math friend. He said a vector is magnitude plus velocity.
It should be magnitude plus orientation, not velocity. Velocity itself is a vector quantity
As a mathematician this genuinely hurts. Lol.
A vector is a list of numbers, at its most basic. You can add a lot of extra functionality to it, but at its core, its just a list.
Functions from the reals to the reals are an example of a vector space with elements which can not be represented as a list of numbers.
It still can be, just not on infinite precision as nothing can with fp.
But the vector space of (all) real functions is a completely different beast from the space of computable functions on finite-precision numbers. If you restrict the equality of these functions to their extension,
defined as f = g iff forall x\in R: f(x)=g(x),
then that vector space appears to be not only finite dimensional, but in fact finite. Otherwise you probably get a countably infinite dimensional vector space indexed by lambda terms (or whatever formalism you prefer.) But nothing like the space which contains vectors like
F_{x_0}(x) := (1 if x = x_0; 0 otherwise)
where x_0 is uncomputable.
This might hit harder if it weren’t for the fact that words very can have multiple senses
