Floating point linear interpolation

Disregarding differences in precision, that expression is equivalent to

float lerp(float a, float b, float f)
{
    return a + f * (b - a);
}

That's 2 additions/subtractions and 1 multiplication instead of 2 addition/subtractions and 2 multiplications.

Presuming floating-point math is available, the OP's algorithm is a good one and is always superior to the alternative a + f * (b - a) due to precision loss when a and b significantly differ in magnitude.

For example:

// OP's algorithm
float lint1 (float a, float b, float f) {
    return (a * (1.0f - f)) + (b * f);
}

// Algebraically simplified algorithm
float lint2 (float a, float b, float f) {
    return a + f * (b - a);
}

In that example, presuming 32-bit floats lint1(1.0e20, 1.0, 1.0) will correctly return 1.0, whereas lint2 will incorrectly return 0.0.

The majority of precision loss is in the addition and subtraction operators when the operands differ significantly in magnitude. In the above case, the culprits are the subtraction in b - a, and the addition in a + f * (b - a). The OP's algorithm does not suffer from this due to the components being completely multiplied before addition.

For the a=1e20, b=1 case, here is an example of differing results. Test program:

#include <stdio.h>
#include <math.h>

float lint1 (float a, float b, float f) {
    return (a * (1.0f - f)) + (b * f);
}

float lint2 (float a, float b, float f) {
    return a + f * (b - a);
}

int main () {
    const float a = 1.0e20;
    const float b = 1.0;
    int n;
    for (n = 0; n <= 1024; ++ n) {
        float f = (float)n / 1024.0f;
        float p1 = lint1(a, b, f);
        float p2 = lint2(a, b, f);
        if (p1 != p2) {
            printf("%i %.6f %f %f %.6e\n", n, f, p1, p2, p2 - p1);
        }
    }
    return 0;
}

Output, slightly adjusted for formatting:

    f            lint1               lint2             lint2-lint1
0.828125  17187500894208393216  17187499794696765440  -1.099512e+12
0.890625  10937500768952909824  10937499669441282048  -1.099512e+12
0.914062   8593750447104196608   8593749897348382720  -5.497558e+11
0.945312   5468750384476454912   5468749834720641024  -5.497558e+11
0.957031   4296875223552098304   4296874948674191360  -2.748779e+11
0.972656   2734375192238227456   2734374917360320512  -2.748779e+11
0.978516   2148437611776049152   2148437474337095680  -1.374390e+11
0.986328   1367187596119113728   1367187458680160256  -1.374390e+11
0.989258   1074218805888024576   1074218737168547840  -6.871948e+10
0.993164    683593798059556864    683593729340080128  -6.871948e+10
1.000000                     1                     0  -1.000000e+00