Any good advanced Math Libraries? (looking for square root, ln, cumulative distributions)

I’ve been trying to implement a black-scholes pricing model in solidity.

Trying to see if it is possible to estimate the gas prices for this in solidity. Deciding if we should implement it on chain, or it should all be computed off chain and pushed on chain.

Anyone know any good advanced math libraries for square roots, ln, N(x)?

1 Like

Hi @aparnakr,

There is a List of Solidity libraries in the wild in the forum.
I don’t know if any would meet your needs.

There was a discussion on Designing Fixed Point Math in OpenZeppelin Contracts

Hopefully someone in the community can suggest a library.
(I have posted this in a couple of communities)

1 Like

As far as I know this library doesn’t exist. We have thought about log and sqrt before, and we may implement them in the future:

Even if they don’t exist yet, we can think about their feasibility.

The first question we should be asking is what is the time complexity of calculating these functions. Anything that runs in linear time or more is probably not a good fit for the blockchain. N(x) does not look promising in that sense, but I’m not really familiar with algorithms for this kind of function.

N(x) = ...

In your case N(x) seems like the “critical path” that you should have a good look at before proceding. If you do some research about it please share with us!

2 Likes

Bokky suggested maybe having a lookup table for the normal cdf https://en.wikipedia.org/wiki/Standard_normal_table and approximating from there. I’m curious if you had any thoughts around that :slight_smile:

Also have you all looked over https://github.com/abdk-consulting/abdk-libraries-solidity/blob/master/ABDKMath64x64.md before? I was wondering if that is a safe library to use for exponent, ln, sqrt etc.

1 Like

Hi @aparnakr,

Thanks for sharing the suggestion from Bokky.

As for https://github.com/abdk-consulting/abdk-libraries-solidity I don’t know much about the library. The warning from the List of Solidity libraries in the wild applies:

:warning: This is not an endorsement of any kind .
Unless it is mentioned specifically, OpenZeppelin haven’t audited any of these libraries and you should assess their quality and extensively test your integrations with them before using them in production.

One important point to note is the license that applies for the ABDK library. I would recommend checking that this is something that you can use and comply with.

1 Like

I’m no expert but using a lookup table sounds perfect IMO. You will want the lookup table to be encoded in bytecode and not in storage because it would be much more expensive.

3 Likes

Here is a piece of code extracted from BancorFormula.sol.

Note that some parts of it were auto-generated using these Python scripts, all of which depend on these common constants, which you can change in order to control the trade-off between accuracy. and performance (i.e., gas-cost).

For both ln and sqrt, you can use the power function; see more details at the bottom of this response.

As I mentioned at the beginning, I have extracted this from (publicly available) BancorFormula.sol, so hopefully I copied all the necessary pieces and only the necessary pieces:

library BancorFormula {
    uint256 private constant ONE = 1;
    uint8 private constant MIN_PRECISION = 32;
    uint8 private constant MAX_PRECISION = 127;

    /**
      * Auto-generated via 'PrintIntScalingFactors.py'
    */
    uint256 private constant FIXED_1 = 0x080000000000000000000000000000000;
    uint256 private constant FIXED_2 = 0x100000000000000000000000000000000;
    uint256 private constant MAX_NUM = 0x200000000000000000000000000000000;

    /**
      * Auto-generated via 'PrintLn2ScalingFactors.py'
    */
    uint256 private constant LN2_NUMERATOR   = 0x3f80fe03f80fe03f80fe03f80fe03f8;
    uint256 private constant LN2_DENOMINATOR = 0x5b9de1d10bf4103d647b0955897ba80;

    /**
      * Auto-generated via 'PrintFunctionOptimalLog.py' and 'PrintFunctionOptimalExp.py'
    */
    uint256 private constant OPT_LOG_MAX_VAL = 0x15bf0a8b1457695355fb8ac404e7a79e3;
    uint256 private constant OPT_EXP_MAX_VAL = 0x800000000000000000000000000000000;

    /**
      * Auto-generated via 'PrintFunctionConstructor.py'
    */
    uint256[128] private maxExpArray;
    constructor() public {
    //  maxExpArray[  0] = 0x6bffffffffffffffffffffffffffffffff;
    //  maxExpArray[  1] = 0x67ffffffffffffffffffffffffffffffff;
    //  maxExpArray[  2] = 0x637fffffffffffffffffffffffffffffff;
    //  maxExpArray[  3] = 0x5f6fffffffffffffffffffffffffffffff;
    //  maxExpArray[  4] = 0x5b77ffffffffffffffffffffffffffffff;
    //  maxExpArray[  5] = 0x57b3ffffffffffffffffffffffffffffff;
    //  maxExpArray[  6] = 0x5419ffffffffffffffffffffffffffffff;
    //  maxExpArray[  7] = 0x50a2ffffffffffffffffffffffffffffff;
    //  maxExpArray[  8] = 0x4d517fffffffffffffffffffffffffffff;
    //  maxExpArray[  9] = 0x4a233fffffffffffffffffffffffffffff;
    //  maxExpArray[ 10] = 0x47165fffffffffffffffffffffffffffff;
    //  maxExpArray[ 11] = 0x4429afffffffffffffffffffffffffffff;
    //  maxExpArray[ 12] = 0x415bc7ffffffffffffffffffffffffffff;
    //  maxExpArray[ 13] = 0x3eab73ffffffffffffffffffffffffffff;
    //  maxExpArray[ 14] = 0x3c1771ffffffffffffffffffffffffffff;
    //  maxExpArray[ 15] = 0x399e96ffffffffffffffffffffffffffff;
    //  maxExpArray[ 16] = 0x373fc47fffffffffffffffffffffffffff;
    //  maxExpArray[ 17] = 0x34f9e8ffffffffffffffffffffffffffff;
    //  maxExpArray[ 18] = 0x32cbfd5fffffffffffffffffffffffffff;
    //  maxExpArray[ 19] = 0x30b5057fffffffffffffffffffffffffff;
    //  maxExpArray[ 20] = 0x2eb40f9fffffffffffffffffffffffffff;
    //  maxExpArray[ 21] = 0x2cc8340fffffffffffffffffffffffffff;
    //  maxExpArray[ 22] = 0x2af09481ffffffffffffffffffffffffff;
    //  maxExpArray[ 23] = 0x292c5bddffffffffffffffffffffffffff;
    //  maxExpArray[ 24] = 0x277abdcdffffffffffffffffffffffffff;
    //  maxExpArray[ 25] = 0x25daf6657fffffffffffffffffffffffff;
    //  maxExpArray[ 26] = 0x244c49c65fffffffffffffffffffffffff;
    //  maxExpArray[ 27] = 0x22ce03cd5fffffffffffffffffffffffff;
    //  maxExpArray[ 28] = 0x215f77c047ffffffffffffffffffffffff;
    //  maxExpArray[ 29] = 0x1fffffffffffffffffffffffffffffffff;
    //  maxExpArray[ 30] = 0x1eaefdbdabffffffffffffffffffffffff;
    //  maxExpArray[ 31] = 0x1d6bd8b2ebffffffffffffffffffffffff;
        maxExpArray[ 32] = 0x1c35fedd14ffffffffffffffffffffffff;
        maxExpArray[ 33] = 0x1b0ce43b323fffffffffffffffffffffff;
        maxExpArray[ 34] = 0x19f0028ec1ffffffffffffffffffffffff;
        maxExpArray[ 35] = 0x18ded91f0e7fffffffffffffffffffffff;
        maxExpArray[ 36] = 0x17d8ec7f0417ffffffffffffffffffffff;
        maxExpArray[ 37] = 0x16ddc6556cdbffffffffffffffffffffff;
        maxExpArray[ 38] = 0x15ecf52776a1ffffffffffffffffffffff;
        maxExpArray[ 39] = 0x15060c256cb2ffffffffffffffffffffff;
        maxExpArray[ 40] = 0x1428a2f98d72ffffffffffffffffffffff;
        maxExpArray[ 41] = 0x13545598e5c23fffffffffffffffffffff;
        maxExpArray[ 42] = 0x1288c4161ce1dfffffffffffffffffffff;
        maxExpArray[ 43] = 0x11c592761c666fffffffffffffffffffff;
        maxExpArray[ 44] = 0x110a688680a757ffffffffffffffffffff;
        maxExpArray[ 45] = 0x1056f1b5bedf77ffffffffffffffffffff;
        maxExpArray[ 46] = 0x0faadceceeff8bffffffffffffffffffff;
        maxExpArray[ 47] = 0x0f05dc6b27edadffffffffffffffffffff;
        maxExpArray[ 48] = 0x0e67a5a25da4107fffffffffffffffffff;
        maxExpArray[ 49] = 0x0dcff115b14eedffffffffffffffffffff;
        maxExpArray[ 50] = 0x0d3e7a392431239fffffffffffffffffff;
        maxExpArray[ 51] = 0x0cb2ff529eb71e4fffffffffffffffffff;
        maxExpArray[ 52] = 0x0c2d415c3db974afffffffffffffffffff;
        maxExpArray[ 53] = 0x0bad03e7d883f69bffffffffffffffffff;
        maxExpArray[ 54] = 0x0b320d03b2c343d5ffffffffffffffffff;
        maxExpArray[ 55] = 0x0abc25204e02828dffffffffffffffffff;
        maxExpArray[ 56] = 0x0a4b16f74ee4bb207fffffffffffffffff;
        maxExpArray[ 57] = 0x09deaf736ac1f569ffffffffffffffffff;
        maxExpArray[ 58] = 0x0976bd9952c7aa957fffffffffffffffff;
        maxExpArray[ 59] = 0x09131271922eaa606fffffffffffffffff;
        maxExpArray[ 60] = 0x08b380f3558668c46fffffffffffffffff;
        maxExpArray[ 61] = 0x0857ddf0117efa215bffffffffffffffff;
        maxExpArray[ 62] = 0x07ffffffffffffffffffffffffffffffff;
        maxExpArray[ 63] = 0x07abbf6f6abb9d087fffffffffffffffff;
        maxExpArray[ 64] = 0x075af62cbac95f7dfa7fffffffffffffff;
        maxExpArray[ 65] = 0x070d7fb7452e187ac13fffffffffffffff;
        maxExpArray[ 66] = 0x06c3390ecc8af379295fffffffffffffff;
        maxExpArray[ 67] = 0x067c00a3b07ffc01fd6fffffffffffffff;
        maxExpArray[ 68] = 0x0637b647c39cbb9d3d27ffffffffffffff;
        maxExpArray[ 69] = 0x05f63b1fc104dbd39587ffffffffffffff;
        maxExpArray[ 70] = 0x05b771955b36e12f7235ffffffffffffff;
        maxExpArray[ 71] = 0x057b3d49dda84556d6f6ffffffffffffff;
        maxExpArray[ 72] = 0x054183095b2c8ececf30ffffffffffffff;
        maxExpArray[ 73] = 0x050a28be635ca2b888f77fffffffffffff;
        maxExpArray[ 74] = 0x04d5156639708c9db33c3fffffffffffff;
        maxExpArray[ 75] = 0x04a23105873875bd52dfdfffffffffffff;
        maxExpArray[ 76] = 0x0471649d87199aa990756fffffffffffff;
        maxExpArray[ 77] = 0x04429a21a029d4c1457cfbffffffffffff;
        maxExpArray[ 78] = 0x0415bc6d6fb7dd71af2cb3ffffffffffff;
        maxExpArray[ 79] = 0x03eab73b3bbfe282243ce1ffffffffffff;
        maxExpArray[ 80] = 0x03c1771ac9fb6b4c18e229ffffffffffff;
        maxExpArray[ 81] = 0x0399e96897690418f785257fffffffffff;
        maxExpArray[ 82] = 0x0373fc456c53bb779bf0ea9fffffffffff;
        maxExpArray[ 83] = 0x034f9e8e490c48e67e6ab8bfffffffffff;
        maxExpArray[ 84] = 0x032cbfd4a7adc790560b3337ffffffffff;
        maxExpArray[ 85] = 0x030b50570f6e5d2acca94613ffffffffff;
        maxExpArray[ 86] = 0x02eb40f9f620fda6b56c2861ffffffffff;
        maxExpArray[ 87] = 0x02cc8340ecb0d0f520a6af58ffffffffff;
        maxExpArray[ 88] = 0x02af09481380a0a35cf1ba02ffffffffff;
        maxExpArray[ 89] = 0x0292c5bdd3b92ec810287b1b3fffffffff;
        maxExpArray[ 90] = 0x0277abdcdab07d5a77ac6d6b9fffffffff;
        maxExpArray[ 91] = 0x025daf6654b1eaa55fd64df5efffffffff;
        maxExpArray[ 92] = 0x0244c49c648baa98192dce88b7ffffffff;
        maxExpArray[ 93] = 0x022ce03cd5619a311b2471268bffffffff;
        maxExpArray[ 94] = 0x0215f77c045fbe885654a44a0fffffffff;
        maxExpArray[ 95] = 0x01ffffffffffffffffffffffffffffffff;
        maxExpArray[ 96] = 0x01eaefdbdaaee7421fc4d3ede5ffffffff;
        maxExpArray[ 97] = 0x01d6bd8b2eb257df7e8ca57b09bfffffff;
        maxExpArray[ 98] = 0x01c35fedd14b861eb0443f7f133fffffff;
        maxExpArray[ 99] = 0x01b0ce43b322bcde4a56e8ada5afffffff;
        maxExpArray[100] = 0x019f0028ec1fff007f5a195a39dfffffff;
        maxExpArray[101] = 0x018ded91f0e72ee74f49b15ba527ffffff;
        maxExpArray[102] = 0x017d8ec7f04136f4e5615fd41a63ffffff;
        maxExpArray[103] = 0x016ddc6556cdb84bdc8d12d22e6fffffff;
        maxExpArray[104] = 0x015ecf52776a1155b5bd8395814f7fffff;
        maxExpArray[105] = 0x015060c256cb23b3b3cc3754cf40ffffff;
        maxExpArray[106] = 0x01428a2f98d728ae223ddab715be3fffff;
        maxExpArray[107] = 0x013545598e5c23276ccf0ede68034fffff;
        maxExpArray[108] = 0x01288c4161ce1d6f54b7f61081194fffff;
        maxExpArray[109] = 0x011c592761c666aa641d5a01a40f17ffff;
        maxExpArray[110] = 0x0110a688680a7530515f3e6e6cfdcdffff;
        maxExpArray[111] = 0x01056f1b5bedf75c6bcb2ce8aed428ffff;
        maxExpArray[112] = 0x00faadceceeff8a0890f3875f008277fff;
        maxExpArray[113] = 0x00f05dc6b27edad306388a600f6ba0bfff;
        maxExpArray[114] = 0x00e67a5a25da41063de1495d5b18cdbfff;
        maxExpArray[115] = 0x00dcff115b14eedde6fc3aa5353f2e4fff;
        maxExpArray[116] = 0x00d3e7a3924312399f9aae2e0f868f8fff;
        maxExpArray[117] = 0x00cb2ff529eb71e41582cccd5a1ee26fff;
        maxExpArray[118] = 0x00c2d415c3db974ab32a51840c0b67edff;
        maxExpArray[119] = 0x00bad03e7d883f69ad5b0a186184e06bff;
        maxExpArray[120] = 0x00b320d03b2c343d4829abd6075f0cc5ff;
        maxExpArray[121] = 0x00abc25204e02828d73c6e80bcdb1a95bf;
        maxExpArray[122] = 0x00a4b16f74ee4bb2040a1ec6c15fbbf2df;
        maxExpArray[123] = 0x009deaf736ac1f569deb1b5ae3f36c130f;
        maxExpArray[124] = 0x00976bd9952c7aa957f5937d790ef65037;
        maxExpArray[125] = 0x009131271922eaa6064b73a22d0bd4f2bf;
        maxExpArray[126] = 0x008b380f3558668c46c91c49a2f8e967b9;
        maxExpArray[127] = 0x00857ddf0117efa215952912839f6473e6;
    }

    /**
      * @dev General Description:
      *     Determine a value of precision.
      *     Calculate an integer approximation of (_baseN / _baseD) ^ (_expN / _expD) * 2 ^ precision.
      *     Return the result along with the precision used.
      *
      * Detailed Description:
      *     Instead of calculating "base ^ exp", we calculate "e ^ (log(base) * exp)".
      *     The value of "log(base)" is represented with an integer slightly smaller than "log(base) * 2 ^ precision".
      *     The larger "precision" is, the more accurately this value represents the real value.
      *     However, the larger "precision" is, the more bits are required in order to store this value.
      *     And the exponentiation function, which takes "x" and calculates "e ^ x", is limited to a maximum exponent (maximum value of "x").
      *     This maximum exponent depends on the "precision" used, and it is given by "maxExpArray[precision] >> (MAX_PRECISION - precision)".
      *     Hence we need to determine the highest precision which can be used for the given input, before calling the exponentiation function.
      *     This allows us to compute "base ^ exp" with maximum accuracy and without exceeding 256 bits in any of the intermediate computations.
      *     This functions assumes that "_expN < 2 ^ 256 / log(MAX_NUM - 1)", otherwise the multiplication should be replaced with a "safeMul".
      *     Since we rely on unsigned-integer arithmetic and "base < 1" ==> "log(base) < 0", this function does not support "_baseN < _baseD".
    */
    function power(uint256 _baseN, uint256 _baseD, uint32 _expN, uint32 _expD) internal view returns (uint256, uint8) {
        require(_baseN < MAX_NUM);

        uint256 baseLog;
        uint256 base = _baseN * FIXED_1 / _baseD;
        if (base < OPT_LOG_MAX_VAL) {
            baseLog = optimalLog(base);
        }
        else {
            baseLog = generalLog(base);
        }

        uint256 baseLogTimesExp = baseLog * _expN / _expD;
        if (baseLogTimesExp < OPT_EXP_MAX_VAL) {
            return (optimalExp(baseLogTimesExp), MAX_PRECISION);
        }
        else {
            uint8 precision = findPositionInMaxExpArray(baseLogTimesExp);
            return (generalExp(baseLogTimesExp >> (MAX_PRECISION - precision), precision), precision);
        }
    }

    /**
      * @dev computes log(x / FIXED_1) * FIXED_1.
      * This functions assumes that "x >= FIXED_1", because the output would be negative otherwise.
    */
    function generalLog(uint256 x) internal pure returns (uint256) {
        uint256 res = 0;

        // If x >= 2, then we compute the integer part of log2(x), which is larger than 0.
        if (x >= FIXED_2) {
            uint8 count = floorLog2(x / FIXED_1);
            x >>= count; // now x < 2
            res = count * FIXED_1;
        }

        // If x > 1, then we compute the fraction part of log2(x), which is larger than 0.
        if (x > FIXED_1) {
            for (uint8 i = MAX_PRECISION; i > 0; --i) {
                x = (x * x) / FIXED_1; // now 1 < x < 4
                if (x >= FIXED_2) {
                    x >>= 1; // now 1 < x < 2
                    res += ONE << (i - 1);
                }
            }
        }

        return res * LN2_NUMERATOR / LN2_DENOMINATOR;
    }

    /**
      * @dev computes the largest integer smaller than or equal to the binary logarithm of the input.
    */
    function floorLog2(uint256 _n) internal pure returns (uint8) {
        uint8 res = 0;

        if (_n < 256) {
            // At most 8 iterations
            while (_n > 1) {
                _n >>= 1;
                res += 1;
            }
        }
        else {
            // Exactly 8 iterations
            for (uint8 s = 128; s > 0; s >>= 1) {
                if (_n >= (ONE << s)) {
                    _n >>= s;
                    res |= s;
                }
            }
        }

        return res;
    }

    /**
      * @dev the global "maxExpArray" is sorted in descending order, and therefore the following statements are equivalent:
      * - This function finds the position of [the smallest value in "maxExpArray" larger than or equal to "x"]
      * - This function finds the highest position of [a value in "maxExpArray" larger than or equal to "x"]
    */
    function findPositionInMaxExpArray(uint256 _x) internal view returns (uint8) {
        uint8 lo = MIN_PRECISION;
        uint8 hi = MAX_PRECISION;

        while (lo + 1 < hi) {
            uint8 mid = (lo + hi) / 2;
            if (maxExpArray[mid] >= _x)
                lo = mid;
            else
                hi = mid;
        }

        if (maxExpArray[hi] >= _x)
            return hi;
        if (maxExpArray[lo] >= _x)
            return lo;

        require(false);
        return 0;
    }

    /**
      * @dev this function can be auto-generated by the script 'PrintFunctionGeneralExp.py'.
      * it approximates "e ^ x" via maclaurin summation: "(x^0)/0! + (x^1)/1! + ... + (x^n)/n!".
      * it returns "e ^ (x / 2 ^ precision) * 2 ^ precision", that is, the result is upshifted for accuracy.
      * the global "maxExpArray" maps each "precision" to "((maximumExponent + 1) << (MAX_PRECISION - precision)) - 1".
      * the maximum permitted value for "x" is therefore given by "maxExpArray[precision] >> (MAX_PRECISION - precision)".
    */
    function generalExp(uint256 _x, uint8 _precision) internal pure returns (uint256) {
        uint256 xi = _x;
        uint256 res = 0;

        xi = (xi * _x) >> _precision; res += xi * 0x3442c4e6074a82f1797f72ac0000000; // add x^02 * (33! / 02!)
        xi = (xi * _x) >> _precision; res += xi * 0x116b96f757c380fb287fd0e40000000; // add x^03 * (33! / 03!)
        xi = (xi * _x) >> _precision; res += xi * 0x045ae5bdd5f0e03eca1ff4390000000; // add x^04 * (33! / 04!)
        xi = (xi * _x) >> _precision; res += xi * 0x00defabf91302cd95b9ffda50000000; // add x^05 * (33! / 05!)
        xi = (xi * _x) >> _precision; res += xi * 0x002529ca9832b22439efff9b8000000; // add x^06 * (33! / 06!)
        xi = (xi * _x) >> _precision; res += xi * 0x00054f1cf12bd04e516b6da88000000; // add x^07 * (33! / 07!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000a9e39e257a09ca2d6db51000000; // add x^08 * (33! / 08!)
        xi = (xi * _x) >> _precision; res += xi * 0x000012e066e7b839fa050c309000000; // add x^09 * (33! / 09!)
        xi = (xi * _x) >> _precision; res += xi * 0x000001e33d7d926c329a1ad1a800000; // add x^10 * (33! / 10!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000002bee513bdb4a6b19b5f800000; // add x^11 * (33! / 11!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000003a9316fa79b88eccf2a00000; // add x^12 * (33! / 12!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000048177ebe1fa812375200000; // add x^13 * (33! / 13!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000005263fe90242dcbacf00000; // add x^14 * (33! / 14!)
        xi = (xi * _x) >> _precision; res += xi * 0x000000000057e22099c030d94100000; // add x^15 * (33! / 15!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000057e22099c030d9410000; // add x^16 * (33! / 16!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000052b6b54569976310000; // add x^17 * (33! / 17!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000004985f67696bf748000; // add x^18 * (33! / 18!)
        xi = (xi * _x) >> _precision; res += xi * 0x000000000000003dea12ea99e498000; // add x^19 * (33! / 19!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000000031880f2214b6e000; // add x^20 * (33! / 20!)
        xi = (xi * _x) >> _precision; res += xi * 0x000000000000000025bcff56eb36000; // add x^21 * (33! / 21!)
        xi = (xi * _x) >> _precision; res += xi * 0x000000000000000001b722e10ab1000; // add x^22 * (33! / 22!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000000000001317c70077000; // add x^23 * (33! / 23!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000000000000cba84aafa00; // add x^24 * (33! / 24!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000000000000082573a0a00; // add x^25 * (33! / 25!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000000000000005035ad900; // add x^26 * (33! / 26!)
        xi = (xi * _x) >> _precision; res += xi * 0x000000000000000000000002f881b00; // add x^27 * (33! / 27!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000000000000000001b29340; // add x^28 * (33! / 28!)
        xi = (xi * _x) >> _precision; res += xi * 0x00000000000000000000000000efc40; // add x^29 * (33! / 29!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000000000000000000007fe0; // add x^30 * (33! / 30!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000000000000000000000420; // add x^31 * (33! / 31!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000000000000000000000021; // add x^32 * (33! / 32!)
        xi = (xi * _x) >> _precision; res += xi * 0x0000000000000000000000000000001; // add x^33 * (33! / 33!)

        return res / 0x688589cc0e9505e2f2fee5580000000 + _x + (ONE << _precision); // divide by 33! and then add x^1 / 1! + x^0 / 0!
    }

    /**
      * @dev computes log(x / FIXED_1) * FIXED_1
      * Input range: FIXED_1 <= x <= LOG_EXP_MAX_VAL - 1
      * Auto-generated via 'PrintFunctionOptimalLog.py'
      * Detailed description:
      * - Rewrite the input as a product of natural exponents and a single residual r, such that 1 < r < 2
      * - The natural logarithm of each (pre-calculated) exponent is the degree of the exponent
      * - The natural logarithm of r is calculated via Taylor series for log(1 + x), where x = r - 1
      * - The natural logarithm of the input is calculated by summing up the intermediate results above
      * - For example: log(250) = log(e^4 * e^1 * e^0.5 * 1.021692859) = 4 + 1 + 0.5 + log(1 + 0.021692859)
    */
    function optimalLog(uint256 x) internal pure returns (uint256) {
        uint256 res = 0;

        uint256 y;
        uint256 z;
        uint256 w;

        if (x >= 0xd3094c70f034de4b96ff7d5b6f99fcd8) {res += 0x40000000000000000000000000000000; x = x * FIXED_1 / 0xd3094c70f034de4b96ff7d5b6f99fcd8;} // add 1 / 2^1
        if (x >= 0xa45af1e1f40c333b3de1db4dd55f29a7) {res += 0x20000000000000000000000000000000; x = x * FIXED_1 / 0xa45af1e1f40c333b3de1db4dd55f29a7;} // add 1 / 2^2
        if (x >= 0x910b022db7ae67ce76b441c27035c6a1) {res += 0x10000000000000000000000000000000; x = x * FIXED_1 / 0x910b022db7ae67ce76b441c27035c6a1;} // add 1 / 2^3
        if (x >= 0x88415abbe9a76bead8d00cf112e4d4a8) {res += 0x08000000000000000000000000000000; x = x * FIXED_1 / 0x88415abbe9a76bead8d00cf112e4d4a8;} // add 1 / 2^4
        if (x >= 0x84102b00893f64c705e841d5d4064bd3) {res += 0x04000000000000000000000000000000; x = x * FIXED_1 / 0x84102b00893f64c705e841d5d4064bd3;} // add 1 / 2^5
        if (x >= 0x8204055aaef1c8bd5c3259f4822735a2) {res += 0x02000000000000000000000000000000; x = x * FIXED_1 / 0x8204055aaef1c8bd5c3259f4822735a2;} // add 1 / 2^6
        if (x >= 0x810100ab00222d861931c15e39b44e99) {res += 0x01000000000000000000000000000000; x = x * FIXED_1 / 0x810100ab00222d861931c15e39b44e99;} // add 1 / 2^7
        if (x >= 0x808040155aabbbe9451521693554f733) {res += 0x00800000000000000000000000000000; x = x * FIXED_1 / 0x808040155aabbbe9451521693554f733;} // add 1 / 2^8

        z = y = x - FIXED_1;
        w = y * y / FIXED_1;
        res += z * (0x100000000000000000000000000000000 - y) / 0x100000000000000000000000000000000; z = z * w / FIXED_1; // add y^01 / 01 - y^02 / 02
        res += z * (0x0aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa - y) / 0x200000000000000000000000000000000; z = z * w / FIXED_1; // add y^03 / 03 - y^04 / 04
        res += z * (0x099999999999999999999999999999999 - y) / 0x300000000000000000000000000000000; z = z * w / FIXED_1; // add y^05 / 05 - y^06 / 06
        res += z * (0x092492492492492492492492492492492 - y) / 0x400000000000000000000000000000000; z = z * w / FIXED_1; // add y^07 / 07 - y^08 / 08
        res += z * (0x08e38e38e38e38e38e38e38e38e38e38e - y) / 0x500000000000000000000000000000000; z = z * w / FIXED_1; // add y^09 / 09 - y^10 / 10
        res += z * (0x08ba2e8ba2e8ba2e8ba2e8ba2e8ba2e8b - y) / 0x600000000000000000000000000000000; z = z * w / FIXED_1; // add y^11 / 11 - y^12 / 12
        res += z * (0x089d89d89d89d89d89d89d89d89d89d89 - y) / 0x700000000000000000000000000000000; z = z * w / FIXED_1; // add y^13 / 13 - y^14 / 14
        res += z * (0x088888888888888888888888888888888 - y) / 0x800000000000000000000000000000000;                      // add y^15 / 15 - y^16 / 16

        return res;
    }

    /**
      * @dev computes e ^ (x / FIXED_1) * FIXED_1
      * input range: 0 <= x <= OPT_EXP_MAX_VAL - 1
      * auto-generated via 'PrintFunctionOptimalExp.py'
      * Detailed description:
      * - Rewrite the input as a sum of binary exponents and a single residual r, as small as possible
      * - The exponentiation of each binary exponent is given (pre-calculated)
      * - The exponentiation of r is calculated via Taylor series for e^x, where x = r
      * - The exponentiation of the input is calculated by multiplying the intermediate results above
      * - For example: e^5.521692859 = e^(4 + 1 + 0.5 + 0.021692859) = e^4 * e^1 * e^0.5 * e^0.021692859
    */
    function optimalExp(uint256 x) internal pure returns (uint256) {
        uint256 res = 0;

        uint256 y;
        uint256 z;

        z = y = x % 0x10000000000000000000000000000000; // get the input modulo 2^(-3)
        z = z * y / FIXED_1; res += z * 0x10e1b3be415a0000; // add y^02 * (20! / 02!)
        z = z * y / FIXED_1; res += z * 0x05a0913f6b1e0000; // add y^03 * (20! / 03!)
        z = z * y / FIXED_1; res += z * 0x0168244fdac78000; // add y^04 * (20! / 04!)
        z = z * y / FIXED_1; res += z * 0x004807432bc18000; // add y^05 * (20! / 05!)
        z = z * y / FIXED_1; res += z * 0x000c0135dca04000; // add y^06 * (20! / 06!)
        z = z * y / FIXED_1; res += z * 0x0001b707b1cdc000; // add y^07 * (20! / 07!)
        z = z * y / FIXED_1; res += z * 0x000036e0f639b800; // add y^08 * (20! / 08!)
        z = z * y / FIXED_1; res += z * 0x00000618fee9f800; // add y^09 * (20! / 09!)
        z = z * y / FIXED_1; res += z * 0x0000009c197dcc00; // add y^10 * (20! / 10!)
        z = z * y / FIXED_1; res += z * 0x0000000e30dce400; // add y^11 * (20! / 11!)
        z = z * y / FIXED_1; res += z * 0x000000012ebd1300; // add y^12 * (20! / 12!)
        z = z * y / FIXED_1; res += z * 0x0000000017499f00; // add y^13 * (20! / 13!)
        z = z * y / FIXED_1; res += z * 0x0000000001a9d480; // add y^14 * (20! / 14!)
        z = z * y / FIXED_1; res += z * 0x00000000001c6380; // add y^15 * (20! / 15!)
        z = z * y / FIXED_1; res += z * 0x000000000001c638; // add y^16 * (20! / 16!)
        z = z * y / FIXED_1; res += z * 0x0000000000001ab8; // add y^17 * (20! / 17!)
        z = z * y / FIXED_1; res += z * 0x000000000000017c; // add y^18 * (20! / 18!)
        z = z * y / FIXED_1; res += z * 0x0000000000000014; // add y^19 * (20! / 19!)
        z = z * y / FIXED_1; res += z * 0x0000000000000001; // add y^20 * (20! / 20!)
        res = res / 0x21c3677c82b40000 + y + FIXED_1; // divide by 20! and then add y^1 / 1! + y^0 / 0!

        if ((x & 0x010000000000000000000000000000000) != 0) res = res * 0x1c3d6a24ed82218787d624d3e5eba95f9 / 0x18ebef9eac820ae8682b9793ac6d1e776; // multiply by e^2^(-3)
        if ((x & 0x020000000000000000000000000000000) != 0) res = res * 0x18ebef9eac820ae8682b9793ac6d1e778 / 0x1368b2fc6f9609fe7aceb46aa619baed4; // multiply by e^2^(-2)
        if ((x & 0x040000000000000000000000000000000) != 0) res = res * 0x1368b2fc6f9609fe7aceb46aa619baed5 / 0x0bc5ab1b16779be3575bd8f0520a9f21f; // multiply by e^2^(-1)
        if ((x & 0x080000000000000000000000000000000) != 0) res = res * 0x0bc5ab1b16779be3575bd8f0520a9f21e / 0x0454aaa8efe072e7f6ddbab84b40a55c9; // multiply by e^2^(+0)
        if ((x & 0x100000000000000000000000000000000) != 0) res = res * 0x0454aaa8efe072e7f6ddbab84b40a55c5 / 0x00960aadc109e7a3bf4578099615711ea; // multiply by e^2^(+1)
        if ((x & 0x200000000000000000000000000000000) != 0) res = res * 0x00960aadc109e7a3bf4578099615711d7 / 0x0002bf84208204f5977f9a8cf01fdce3d; // multiply by e^2^(+2)
        if ((x & 0x400000000000000000000000000000000) != 0) res = res * 0x0002bf84208204f5977f9a8cf01fdc307 / 0x0000003c6ab775dd0b95b4cbee7e65d11; // multiply by e^2^(+3)

        return res;
    }
}

For the square root of integer x, you can use power(x, 1, 1, 2), which will return:

  • The square root of the input, scaled up to precision bits
  • The value of precision

So at some point after retrieving this result (for example, when you are worried that the intermediate result of multiplying it is going to overflow), you should scale it down accordingly (i.e., shift it right by precision bits).

Alternatively, you can return it as a tuple of:

  • Numerator (the result)
  • Denominator - uint256(1) << precision

For the natural logarithm, you can use the first part of the power function:

    function ln(uint256 _baseN, uint256 _baseD) internal view returns (uint256) {
        require(_baseN < MAX_NUM);

        uint256 base = _baseN * FIXED_1 / _baseD;
        if (base < OPT_LOG_MAX_VAL) {
            return optimalLog(base);
        }
        else {
            return generalLog(base);
        }
    }

The function above returns the natural logarithm of the input, scaled up to MAX_PRECISION (127) bits.

So at some point after retrieving this result (for example, when you are worried that the intermediate result of multiplying it is going to overflow), you should scale it down accordingly (i.e., shift it right by 127 bits).

Alternatively, you can return it as a tuple of:

  • Numerator (the result)
  • Denominator - uint256(1) << MAX_PRECISION, or simply FIXED_1
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In important note from the documentation of the power function

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