# Notes on Vector Libraries

05/12/2019

A good vector math library is essential for graphics and simulation programming. However, implementing one that is flexible, efficient, and easy to use is difficult. Due to so many choices, experienced programmers tend to write their own to accommodate their preference.

In this article I will survey a few of the most popular techniques and offer some design advice. I will specifically focus on math theory and C implementations.

## Math

Before diving into the code. It’s helpful to review some of the math to understand what we are aiming for. One thing to watch for is operations that can be defined in terms of each other. Rarely do I see libraries take advantage of this.

Vector Operations

On vectors in `R^N`

• addition `v + w`
• subtraction `v - w`. Defined by addition: `a - b = a + (-b)`
• multiplication `v * w`
• scaling `a * v`
• normalization. Defined by length and scaling: `1/|v| * v`

From `R^N -> R`

• dot product `<v, w>`
• length `|v|`. Defined by dot product `sqrt(<v,v>)`
• angle. Defined by dot product and length `acos(<a,b>/|a||b|)`

Only on specific dimension, such as `R^2` or `R^3`

• cross product `a X b`
• angle (in the plane)

Matrix Operations

On all matrices `M(n x m)`

• addition `A + B`
• subtraction `A - B` Defined by addition `A - B = A + (-B)`
• scaling `bA`
• multiplication `AB`

On square matrices `M(n x n)`

• determinant `det(A)`
• inverse `A^-1`

Between vectors and matrices

• multiplication `Av`

Most programs use 2, 3, and 4 element vectors, and only a few operations are specific to a given dimension. So a lot of code can be condensed by writing algorithms on N dimensional vectors.

Matrix operations are also very general. But a few should be kept to a specific dimension (usually 3x3 or 4x4). You do not want to implement a general inverse or determinant function.

## 1. Simple Structs

``````typedef struct
{
float x, y, z;
} vec3;

{
vec3 r;
r.x = a.x + b.x;
r.y = a.y + b.y;
r.z = a.z + b.z;
return r;
}
``````

This works well for smaller programs. The best part is that expressions look nice (`a + 2.0*(b-d)`):

``````vec3_add(a, vec3_scale(2.0, vec_sub(b, c)));
``````

But, we have to copy this definition for every dimension. We also have to avoid any algorithms that use index or iteration. Matrix vector multiplication gets ugly.

If you only have a few functions that need indexing and you can index into a pointer to the first member:

``````vec3 a;
float* v = &a.x;
v[0];
``````

Examples

## 2. Arrays

For `N` dimensional vectors we might try to write functions which operate on arrays of floats. This is nice because it does not introduce another data structure, so other functions and vector libraries play nice with each other.

Unfortunately, C does not allow you to return an array from the stack. You can only return a pointer which must point to some valid region. So either we do something horrible like `malloc` in each operation, or pass in arrays for the return value. Passing in arrays works, but it destroys the ability to comfortably write simple expressions such as `a + 2.0*(b-d)`:

``````void vecn_add(int n, float* a, float* b, float* ret);

// intermediate results everywhere
float temp[3];
vecn_sub(3, b, d, temp);

float temp2[3];
vecn_scale(3, 2.0, temp, temp2);

float final[3];
``````

Plain arrays may be appropriate for matrices since they are not typically involved in complex expressions. Matrices and large vectors, which would be inefficient to copy around would also be a good use case.

Depending on the application you may not want to sacrifice performance by introducing loops and branching into every operation. As long as the dimensions are input as a literals or macros, the small loops should be unrolled at compile time.

Examples:

## 3. Struct + Union

A workaround to return an array from a function is to include it in a struct. The tradeoff is that the size must be fixed and element access is a bit uglier as it requires at least an extra letter.

``````typedef struct
{
float e[3];
} vec3;

vec3 v;
v.e[0] = 1;
``````

The access syntax can be cleaned up with a union but, anonymous structs/unions are a GCC extension and are non-standard.

``````typedef union
{
float v[3];
struct
{
float x;
float y;
float z;
};
} vec3;
``````

This gives you safe iterative access and nice named members, but it is hard to combine with generic functions. Either you use functions which operate on the internal arrays and deal with the intermediate results. Or, define fixed dimension functions which wrap the generic ones:

``````vec3 vec3_add(vec3 a, vec3 b);
{
vec3 temp;
return temp;
}
``````

I don’t love this option. If I need to write wrapper functions I might as well go back to method 1 and copy implementations around.

Examples:

## 4. Macros

Some clever macros can help you get the best of both worlds, and parameterize the scalar types. This can be combined with an array or union data structure. But, writing multi-line macros isn’t very fun.

``````#define DEFINE_VEC(T, N, SUF) \
\
void vec##N####SUF##_add(const T *a, const T *b,  T *r) \
{ \
for (int i = 0; i < N; ++i) \
r[i] = a[i] + b[i]; \
} \
``````

Then define the types you need:

``````DEFINE_VEC(float, 2, f);
DEFINE_VEC(float, 3, f);
DEFINE_VEC(float, 4, f);
``````

Usage:

``````vec3f_add(a, b);
``````

Functions which only apply to a specific dimension can be defined outside of the macro:

``````void vec3f_cross(const float* a, const float* b, float* r)
{
// ...
}
``````

Examples:

## Closing Thoughts

In typical C fashion, I believe it is misguided to try to write the one true vector library to serve all purposes. These libraries are bloated and must choose tradeoffs which don’t fit your use case. Instead use the examples above to write to tailor make vector functions as needed.

For further reading, see On Vector Math Libraries. It focuses on C++ and has a few other handy tips. You can also read a discussion which led to these notes.