How to write efficient matrix multiplication(gist.github.com)

     '%t*%' <- function(A,v){
        n <- nrow(A)
        x <- as.matrix(c(A[1,], 0, A[1,][n:2]))
        p <- c(v, rep(0, n))
        h <- as.vector(fft(p)*fft(x))
        out <- Re(pracma::ifft(h)[1:n])
       return(matrix(out, n))
     }

     all.equal(A %t*% v,  A %*% v) #TRUE

    var vop = op=>( (a,b)=>( a.map((v,i)=>op(v,b[i])) ) );
    var vdiff = vop((a,b)=>a-b);
    var vadd = vop((a,b)=>a+b);
    var vdot= (a,b)=>a.reduce( (ac,av,i)=>ac+=av*b[i],0);
    var vlength = a=>Math.sqrt(vdot(a,a));
    var vscale = (a,b)=>a.map(v=>v*b);
    var vdistance = (a,b)=>vlength(vdiff(a,b));
    var vnormalised = (a,b=1)=>vscale(a,b/vlength(a));
    var project = (point, matrix) => matrix.map( p=>vdot(p,[...point,1]));
    var transpose = matrix=> ( matrix.reduce(($, row) => row.map((_, i) => [...($[i] || []), row[i]]), []) );
    var multiply = (a,b,...rest)=>(!b)?a:multiply(a.map( (p=>transpose(b).map(q=>vdot(p,q)))),...rest);

  

    In [1]: A = randn(1000, 1000)
    
    In [2]: %timeit A @ A
    25.9 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
    
    In [3]: A *= 1e-160
    
    In [4]: %timeit A @ A
    5.52 s ± 21.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)