In mathematics and computer science, optimal addition-chain exponentiation is a method of exponentiation by a positive integer power that requires a minimal number of multiplications. Using the form of the shortest addition chain, with multiplication instead of addition, computes the desired exponent (instead of multiple) of the base. (This corresponds to .) Each exponentiation in the chain can be evaluated by multiplying two of the earlier exponentiation results. More generally, addition-chain exponentiation may also refer to exponentiation by non-minimal addition chains constructed by a variety of algorithms (since a shortest addition chain is very difficult to find).
The shortest addition-chain algorithm requires no more multiplications than binary exponentiation and usually less. The first example of where it does better is for a15, where the binary method needs six multiplications but the shortest addition chain requires only five:
a15=a x (a x [a x a2]2)2
a15=([a2]2 x a)3
a15=a3 x ([a3]2)2
| Number of multiplications | Actual exponentiation | Specific implementation of addition chains to do exponentiation | |
|---|---|---|---|
| 0 | a1 | a | |
| 1 | a2 | a × a | |
| 2 | a3 | a × a × a | |
| 2 | a4 | (a × a→b) × b | |
| 3 | a5 | (a × a→b) × b × a | |
| 3 | a6 | (a × a→b) × b × b | |
| 4 | a7 | (a × a→b) × b × b × a | |
| 3 | a8 | ((a × a→b) × b→d) × d | |
| 4 | a9 | (a × a × a→c) × c × c | |
| 4 | a10 | ((a × a→b) × b→d) × d × b | |
| 5 | a11 | ((a × a→b) × b→d) × d × b × a | |
| 4 | a12 | ((a × a→b) × b→d) × d × d | |
| 5 | a13 | ((a × a→b) × b→d) × d × d × a | |
| 5 | a14 | ((a × a→b) × b→d) × d × d × b | |
| 5 | a15 | ((a × a→b) × b × a→e) × e × e | |
| 4 | a16 | (((a × a→b) × b→d) × d→h) × h |
There are also several methods to approximate a shortest addition chain, and which often require fewer multiplications than binary exponentiation; binary exponentiation itself is a suboptimal addition-chain algorithm. The optimal algorithm choice depends on the context (such as the relative cost of the multiplication and the number of times a given exponent is re-used).[2]
The problem of finding the shortest addition chain cannot be solved by dynamic programming, because it does not satisfy the assumption of optimal substructure. That is, it is not sufficient to decompose the power into smaller powers, each of which is computed minimally, since the addition chains for the smaller powers may be related (to share computations). For example, in the shortest addition chain for a15 above, the subproblem for a6 must be computed as (a3)2 since a3 is re-used (as opposed to, say, a6 = a2(a2)2, which also requires three multiplies).
If both multiplication and division are allowed, then an addition-subtraction chain may be used to obtain even fewer total multiplications+divisions (where subtraction corresponds to division). However, the slowness of division compared to multiplication makes this technique unattractive in general. For exponentiation to negative integer powers, on the other hand, since one division is required anyway, an addition-subtraction chain is often beneficial. One such example is a-31, where computing 1/a31 by a shortest addition chain for a31 requires 7 multiplications and one division, whereas the shortest addition-subtraction chain requires 5 multiplications and one division:
a-31=a/((((a2)2)2)2)2
For exponentiation on elliptic curves, the inverse of a point (x, y) is available at no cost, since it is simply (x, -y), and therefore addition-subtraction chains are optimal in this context even for positive integer exponents.[3]