Optimization of terms in Fibonacci language

Recently, I got interested in algorithms, and the Fibonacci sequence caught my attention due to its simplicity.

I was able to piece something together in javascript that calculates the nth term in a fibonacci sequence in less than 15 milliseconds after reading a lot of information on the internet. It reaches 1476 ... 1477 is infinity and 1478 is NaN (according to javascript!)

I am very proud of the code itself, except that it is a monster.

So here's my question: A) is there a faster way to calculate the sequence? B) is there a faster / smaller way to multiply two matrices?

Here's the code:

//Fibonacci sequence generator in JS
//Cobbled together by Salty
m = [[1,0],[0,1]];
odd = [[1,1],[1,0]];
function matrix(a,b) {
    /* 
        Matrix multiplication
        Strassen Algorithm
        Only works with 2x2 matrices.
    */
    c=[[0,0],[0,0]];
    c[0][0]=(a[0][0]*b[0][0])+(a[0][1]*b[1][0]);
    c[0][1]=(a[0][0]*b[0][1])+(a[0][1]*b[1][1]);
    c[1][0]=(a[1][0]*b[0][0])+(a[1][1]*b[1][0]);
    c[1][1]=(a[1][0]*b[0][1])+(a[1][1]*b[1][1]);
    m1=(a[0][0]+a[1][1])*(b[0][0]+b[1][1]);
    m2=(a[1][0]+a[1][1])*b[0][0];
    m3=a[0][0]*(b[0][1]-b[1][1]);
    m4=a[1][1]*(b[1][0]-b[0][0]);
    m5=(a[0][0]+a[0][1])*b[1][1];
    m6=(a[1][0]-a[0][0])*(b[0][0]+b[0][1]);
    m7=(a[0][1]-a[1][1])*(b[1][0]+b[1][1]);
    c[0][0]=m1+m4-m5+m7;
    c[0][1]=m3+m5;
    c[1][0]=m2+m4;
    c[1][1]=m1-m2+m3+m6;
    return c;
}
function fib(n) {
    mat(n-1);
    return m[0][0];
}
function mat(n) {
    if(n > 1) {
        mat(n/2);
        m = matrix(m,m);
    }
    m = (n%2<1) ? m : matrix(m,odd);
}
alert(fib(1476)); //Alerts 1.3069892237633993e+308

      

The matrix function takes two arguments, a and b, and returns a * b, where a and b are 2x2 arrays. Oh, and on the side of the note a magic thing happened ... I converted Strassen's algorithm to JS array notation and it worked on my first try! Fantastic, isn't it?: P

Thanks in advance if you can find an easier way to do this.

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10 replies


Don't speculate, compare:

edit: I added my own matrix implementation using the optimized multiplication functions mentioned in my other answer. This resulted in a significant speedup, but even the implementation of vanilla O (n ^ 3) matrix multiplication with loops was faster than Strassen's algorithm.

<pre><script>

var fib = {};

(function() {
    var sqrt_5  = Math.sqrt(5),
        phi     = (1 + sqrt_5) / 2;

    fib.round = function(n) {
        return Math.floor(Math.pow(phi, n) / sqrt_5 + 0.5);
    };
})();

(function() {
    fib.loop = function(n) {
        var i = 0,
            j = 1;

        while(n--) {
            var tmp = i;
            i = j;
            j += tmp;
        }

        return i;
    };
})();

(function () {
    var cache = [0, 1];

    fib.loop_cached = function(n) {
        if(n >= cache.length) {
            for(var i = cache.length; i <= n; ++i)
                cache[i] = cache[i - 1] + cache[i - 2];
        }

        return cache[n];
    };
})();

(function() {
    //Fibonacci sequence generator in JS
    //Cobbled together by Salty
    var m;
    var odd = [[1,1],[1,0]];

    function matrix(a,b) {
        /*
            Matrix multiplication
            Strassen Algorithm
            Only works with 2x2 matrices.
        */
        var c=[[0,0],[0,0]];
        var m1=(a[0][0]+a[1][1])*(b[0][0]+b[1][1]);
        var m2=(a[1][0]+a[1][1])*b[0][0];
        var m3=a[0][0]*(b[0][1]-b[1][1]);
        var m4=a[1][1]*(b[1][0]-b[0][0]);
        var m5=(a[0][0]+a[0][1])*b[1][1];
        var m6=(a[1][0]-a[0][0])*(b[0][0]+b[0][1]);
        var m7=(a[0][1]-a[1][1])*(b[1][0]+b[1][1]);
        c[0][0]=m1+m4-m5+m7;
        c[0][1]=m3+m5;
        c[1][0]=m2+m4;
        c[1][1]=m1-m2+m3+m6;
        return c;
    }

    function mat(n) {
        if(n > 1) {
            mat(n/2);
            m = matrix(m,m);
        }
        m = (n%2<1) ? m : matrix(m,odd);
    }

    fib.matrix = function(n) {
        m = [[1,0],[0,1]];
        mat(n-1);
        return m[0][0];
    };
})();

(function() {
    var a;

    function square() {
        var a00 = a[0][0],
            a01 = a[0][1],
            a10 = a[1][0],
            a11 = a[1][1];

        var a10_x_a01 = a10 * a01,
            a00_p_a11 = a00 + a11;

        a[0][0] = a10_x_a01 + a00 * a00;
        a[0][1] = a00_p_a11 * a01;
        a[1][0] = a00_p_a11 * a10;
        a[1][1] = a10_x_a01 + a11 * a11;
    }

    function powPlusPlus() {
        var a01 = a[0][1],
            a11 = a[1][1];

        a[0][1] = a[0][0];
        a[1][1] = a[1][0];
        a[0][0] += a01;
        a[1][0] += a11;
    }

    function compute(n) {
        if(n > 1) {
            compute(n >> 1);
            square();
            if(n & 1)
                powPlusPlus();
        }
    }

    fib.matrix_optimised = function(n) {
        if(n == 0)
            return 0;

        a = [[1, 1], [1, 0]];
        compute(n - 1);

        return a[0][0];
    };
})();

(function() {
    var cache = {};
    cache[0] = [[1, 0], [0, 1]];
    cache[1] = [[1, 1], [1, 0]];

    function mult(a, b) {
        return [
            [a[0][0] * b[0][0] + a[0][1] * b[1][0],
                a[0][0] * b[0][1] + a[0][1] * b[1][1]],
            [a[1][0] * b[0][0] + a[1][1] * b[1][0],
                a[1][0] * b[0][1] + a[1][1] * b[1][1]]
        ];
    }

    function compute(n) {
        if(!cache[n]) {
            var n_2 = n >> 1;
            compute(n_2);
            cache[n] = mult(cache[n_2], cache[n_2]);
            if(n & 1)
                cache[n] = mult(cache[1], cache[n]);
        }
    }

    fib.matrix_cached = function(n) {
        if(n == 0)
            return 0;

        compute(--n);

        return cache[n][0][0];
    };
})();

function test(name, func, n, count) {
    var value;

    var start = Number(new Date);
    while(count--)
        value = func(n);
    var end = Number(new Date);

    return 'fib.' + name + '(' + n + ') = ' + value + ' [' +
        (end - start) + 'ms]';
}

for(var func in fib)
    document.writeln(test(func, fib[func], 1450, 10000));

</script></pre>

      

gives



fib.round(1450) = 4.8149675025003456e+302 [20ms]
fib.loop(1450) = 4.81496750250011e+302 [4035ms]
fib.loop_cached(1450) = 4.81496750250011e+302 [8ms]
fib.matrix(1450) = 4.814967502500118e+302 [2201ms]
fib.matrix_optimised(1450) = 4.814967502500113e+302 [585ms]
fib.matrix_cached(1450) = 4.814967502500113e+302 [12ms]

      

Your algorithm is almost as bad as a non-circular loop. Caching is your best bet, followed by a rounding algorithm that gives incorrect results for large ones n

(just like your matrix algorithm).

For less, n

your algorithm performs even worse than everything else:

fib.round(100) = 354224848179263100000 [20ms]
fib.loop(100) = 354224848179262000000 [248ms]
fib.loop_cached(100) = 354224848179262000000 [6ms]
fib.matrix(100) = 354224848179261900000 [1911ms]
fib.matrix_optimised(100) = 354224848179261900000 [380ms]
fib.matrix_cached(100) = 354224848179261900000 [12ms]

      

+11


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There is a closed form solution (no loops) for the nth Fibonacci number.



See Wikipedia.

+6


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There might be a faster way to calculate the values, but I don't think it's necessary.

Calculate them once and in your program output the results as a feeddate string below:

fibdata = [1,1,2,3,5,8,13, ... , 1.3069892237633993e+308];  // 1476 entries.
function fib(n) {
    if ((n < 0) || (n > 1476)) {
        ** Do something exception-like or return INF;
    }
    return fibdata[n];
}

      

Then, the code that you send to your customers. This is an O (1) solution for you.

People often forget about "caching" the solution. I once had to write trigonometric routines for an embedded system, and instead of using endless series to calculate them on the fly, I only had a few lookup tables, 360 records each for each degree of input.

Needless to say, he screamed at the cost of only about 1,000 RAM. The values ​​were stored as 1-byte records, [actual value (0-1) * 16], so we could just search, multiply and shift bits to get the desired value.

+4


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My previous answer is a bit overkill, so I'll post a new one:

You can speed up your algorithm using 2x2 vanilla matrix multiplication i.e. replace your function matrix()

like this:

function matrix(a, b) {
    return [
        [a[0][0] * b[0][0] + a[0][1] * b[1][0],
            a[0][0] * b[0][1] + a[0][1] * b[1][1]],
        [a[1][0] * b[0][0] + a[1][1] * b[1][0],
            a[1][0] * b[0][1] + a[1][1] * b[1][1]]
    ];
}

      

If you want accuracy and speed, use a caching solution. If precision is not a concern but memory consumption, use a rounding solution. A matrix solution only makes sense if you want results for large ones to n

be fast, don't care about precision, and don't want to call the function many times.

edit: You can speed up the computation even further if you use specialized multiplication functions, eliminate common subexpressions, and replace values ​​in an existing array instead of creating a new array:

function square() {
    var a00 = a[0][0],
        a01 = a[0][1],
        a10 = a[1][0],
        a11 = a[1][1];

    var a10_x_a01 = a10 * a01,
        a00_p_a11 = a00 + a11;

    a[0][0] = a10_x_a01 + a00 * a00;
    a[0][1] = a00_p_a11 * a01;
    a[1][0] = a00_p_a11 * a10;
    a[1][1] = a10_x_a01 + a11 * a11;
}

function powPlusPlus() {
    var a01 = a[0][1],
        a11 = a[1][1];

    a[0][1] = a[0][0];
    a[1][1] = a[1][0];
    a[0][0] += a01;
    a[1][0] += a11;
}

      

Note: a

is the name of the global matrix variable.

+2


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How about reminders for results that have already been calculated, for example:

var IterMemoFib = function() {
    var cache = [1, 1];
    var fib = function(n) {
        if (n >= cache.length) {
            for (var i = cache.length; i <= n; i++) {
                cache[i] = cache[i - 2] + cache[i - 1];
            }
        }
        return cache[n];
    }

    return fib;
}();

      

Or, if you need a more general memoization function, extend the prototype Function

:

Function.prototype.memoize = function() {
    var pad  = {};
    var self = this;
    var obj  = arguments.length > 0 ? arguments[i] : null;

    var memoizedFn = function() {
        // Copy the arguments object into an array: allows it to be used as
        // a cache key.
        var args = [];
        for (var i = 0; i < arguments.length; i++) {
            args[i] = arguments[i];
        }

        // Evaluate the memoized function if it hasn't been evaluated with
        // these arguments before.
        if (!(args in pad)) {
            pad[args] = self.apply(obj, arguments);
        }

        return pad[args];
    }

    memoizedFn.unmemoize = function() {
        return self;
    }

    return memoizedFn;
}

//Now, you can apply the memoized function to a normal fibonacci function like such:
Fib = fib.memoize();

      

Note that due to technical browser security restrictions, arguments to memoized functions can only be arrays or scalar values . No objects.

Link: http://talideon.com/weblog/2005/07/javascript-memoization.cfm

+1


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To expand a bit on Dreas answer :

1) cache

should you start with [0, 1]


2) what are you doing with IterMemoFib(5.5)

? ( cache[5.5] == undefined

)

fibonacci = (function () {
  var FIB = [0, 1];

  return function (x) {
    if ((typeof(x) !== 'number') || (x < 0)) return;
    x = Math.floor(x);

    if (x >= FIB.length)
      for (var i = FIB.length; i <= x; i += 1)
        FIB[i] = FIB[i-1] + FIB[i-2];

    return FIB[x];
  }
})();

alert(fibonacci(17));    // 1597 (FIB => [0, 1, ..., 1597]) (length = 17)
alert(fibonacci(400));   // 1.760236806450138e+83 (finds 18 to 400)
alert(fibonacci(1476));  // 1.3069892237633987e+308 (length = 1476)

      


If you don't like silent errors:

// replace...
if ((typeof(x) !== 'number') || (x < 0)) return;

// with...
if (typeof(x) !== 'number') throw new TypeError('Not a Number.');
if (x < 0) throw new RangeError('Not a possible fibonacci index. (' + x + ')');

      

+1


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Here is a very quick solution for calculating the Fibonacci sequence

function fib(n){
    var start = Number(new Date); 
    var field = new Array();
    field[0] = 0;
    field[1] = 1;
    for(var i=2; i<=n; i++)
        field[i] = field[i-2] + field[i-1]
    var end = Number(new Date); 
    return 'fib' + '(' + n + ') = ' + field[n] + ' [' +
        (end - start) + 'ms]';

}

var f = fib(1450)
console.log(f)

      

+1


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Closed form solution in JavaScript:

function fib(n){
   var sqrt5 = Math.sqrt(5);
   var a = (1 + sqrt5)/2;
   var b = (1 - sqrt5)/2;
   var ans = Math.round((Math.pow(a, n) - Math.pow(b, n))/sqrt5);
   return ans;
}

      

Sure, even multiplication starts to take on costs when dealing with huge numbers, but that will give you the answer. As far as I know, due to JavaScript rounding values, it is only accurate to n = 75. In the past, you will get a good grade, but it won't be entirely accurate unless you want to do something complicated like store the value in as a string, then parse them as BigIntegers.

+1


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I just wrote my little implementation using an object to store the results already computed. I wrote it in Node.JS which took 2ms (on my timer) to calculate fibonacci for 1476.

Here's the code split in pure Javascript:

var nums = {}; // Object that stores already computed fibonacci results
function fib(n) { //Function
    var ret; //Variable that holds the return Value
    if (n < 3) return 1; //Fib of 1 and 2 equal 1 => filtered here
    else if (nums.hasOwnProperty(n)) ret = nums[n]; /*if the requested number is 
     already in the object nums, return it from the object, instead of computing */
    else ret = fib( n - 2 ) + fib( n - 1 ); /* if requested number has not
     yet been calculated, do so here */
    nums[n] = ret; // add calculated number to nums objecti
    return ret; //return the value
}

//and finally the function call:
fib(1476)

      

EDIT: I haven't tried to run this in a browser!

EDIT again: I have now done this. try the jsfiddle: jsfiddle fibonacci Time changes from 0 to 2ms

+1


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Much faster algorithm:

const fib = n => fib[n] || (fib[n-1] = fib(n-1)) + fib[n-2];
fib[0] = 0; // Any number you like
fib[1] = 1; // Any number you like

      

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