forked from gavin/bc
An implementation of Unix dc and POSIX bc with GNU and BSD extensions
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# Algorithms

This `bc` uses the math algorithms below:

This `bc` uses brute force addition, which is linear (`O(n)`) in the number of digits.

### Subtraction

This `bc` uses brute force subtraction, which is linear (`O(n)`) in the number of digits.

### Multiplication

This `bc` uses two algorithms: Karatsuba and brute force.

Karatsuba is used for "large" numbers. ("Large" numbers are defined as any number with `BC_NUM_KARATSUBA_LEN` digits or larger. `BC_NUM_KARATSUBA_LEN` has a sane default, but may be configured by the user.) Karatsuba, as implemented in this `bc`, is superlinear but subpolynomial (bounded by `O(n^log_2(3))`).

Brute force multiplication is used below `BC_NUM_KARATSUBA_LEN` digits. It is polynomial (`O(n^2)`), but since Karatsuba requires both more intermediate values (which translate to memory allocations) and a few more additions, there is a "break even" point in the number of digits where brute force multiplication is faster than Karatsuba. There is a script (`\$ROOT/karatsuba.py`) that will find the break even point on a particular machine.

WARNING: The Karatsuba script requires Python 3.

### Division

This `bc` uses Algorithm D (long division). Long division is polynomial (`O(n^2)`), but unlike Karatsuba, any division "divide and conquer" algorithm reaches its "break even" point with significantly larger numbers. "Fast" algorithms become less attractive with division as this operation typically reduces the problem size.

While the implementation of long division may appear to use the subtractive chunking method, it only uses subtraction to find a quotient digit. It avoids unnecessary work by aligning digits prior to performing subtraction and finding a starting guess for the quotient.

Subtraction was used instead of multiplication for two reasons:

1. Division and subtraction can share code (one of the less important goals of this `bc` is small code).
2. It minimizes algorithmic complexity.

Using multiplication would make division have the even worse algorithmic complexity of `O(n^(2*log_2(3)))` (best case) and `O(n^3)` (worst case).

### Power

This `bc` implements Exponentiation by Squaring, which (via Karatsuba) has a complexity of `O((n*log(n))^log_2(3))` which is favorable to the `O((n*log(n))^2)` without Karatsuba.

### Square Root

This `bc` implements the fast algorithm Newton's Method (also known as the Newton-Raphson Method, or the Babylonian Method) to perform the square root operation. Its complexity is `O(log(n)*n^2)` as it requires one division per iteration.

### Sine and Cosine (`bc` Only)

This `bc` uses the series

``````x - x^3/3! + x^5/5! - x^7/7! + ...
``````

to calculate `sin(x)` and `cos(x)`. It also uses the relation

``````cos(x) = sin(x + pi/2)
``````

to calculate `cos(x)`. It has a complexity of `O(n^3)`.

Note: this series has a tendency to occasionally produce an error of 1 ULP. (It is an unfortunate side effect of the algorithm, and there isn't any way around it; this article explains why calculating sine and cosine, and the other transcendental functions below, within less than 1 ULP is nearly impossible and unnecessary.) Therefore, I recommend that users do their calculations with the precision (`scale`) set to at least 1 greater than is needed.

### Exponentiation (`bc` Only)

This `bc` uses the series

``````1 + x + x^2/2! + x^3/3! + ...
``````

to calculate `e^x`. Since this only works when `x` is small, it uses

``````e^x = (e^(x/2))^2
``````

to reduce `x`. It has a complexity of `O(n^3)`.

Note: this series can also produce errors of 1 ULP, so I recommend users do their calculations with the precision (`scale`) set to at least 1 greater than is needed.

### Natural Logarithm (`bc` Only)

This `bc` uses the series

``````a + a^3/3 + a^5/5 + ...
``````

(where `a` is equal to `(x - 1)/(x + 1)`) to calculate `ln(x)` when `x` is small and uses the relation

``````ln(x^2) = 2 * ln(x)
``````

to sufficiently reduce `x`. It has a complexity of `O(n^3)`.

Note: this series can also produce errors of 1 ULP, so I recommend users do their calculations with the precision (`scale`) set to at least 1 greater than is needed.

### Arctangent (`bc` Only)

This `bc` uses the series

``````x - x^3/3 + x^5/5 - x^7/7 + ...
``````

to calculate `atan(x)` for small `x` and the relation

``````atan(x) = atan(c) + atan((x - c)/(1 + x * c))
``````

to reduce `x` to small enough. It has a complexity of `O(n^3)`.

Note: this series can also produce errors of 1 ULP, so I recommend users do their calculations with the precision (`scale`) set to at least 1 greater than is needed.

### Bessel (`bc` Only)

This `bc` uses the series

``````x^n/(2^n * n!) * (1 - x^2 * 2 * 1! * (n + 1)) + x^4/(2^4 * 2! * (n + 1) * (n + 2)) - ...
``````

to calculate the bessel function (integer order only).

It also uses the relation

``````j(-n,x) = (-1)^n * j(n,x)
``````

to calculate the bessel when `x < 0`, It has a complexity of `O(n^3)`.

Note: this series can also produce errors of 1 ULP, so I recommend users do their calculations with the precision (`scale`) set to at least 1 greater than is needed.

### Modular Exponentiation (`dc` Only)

This `dc` uses the Memory-efficient method to compute modular exponentiation. The complexity is `O(e*n^2)`, which may initially seem inefficient, but `n` is kept small by maintaining small numbers. In practice, it is extremely fast.