# Efficiently Calculate Matrices Defined Recursively
**URL:** https://discourse.julialang.org/t/efficiently-calculate-matrices-defined-recursively/25006
**Category:** Performance
**Created:** [June 6, 2019, 12:39pm UTC](https://discourse.julialang.org/t/efficiently-calculate-matrices-defined-recursively/25006 "2019-06-06T12:39:23Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@Joseph\_Saia](https://discourse.julialang.org/u/Joseph_Saia)
#### Post date: [June 6, 2019, 12:39pm UTC](https://discourse.julialang.org/t/efficiently-calculate-matrices-defined-recursively/25006/1 "2019-06-06T12:39:23Z")
In my work I frequently have to calculate a long sequence (about N=10^6) of small matrices (about 3x3) that are defined recursively like `X_t = f(X_{t-1})`. The object of interest is the entire chain of matrices so I need to calculate and save each of them. I can usually write an efficient version of `f()` that can do all the calculations in place. A simplified version of this algorithm is below. My main question is what is the most efficient way to store the sequence of matrices `X` and to access each sub matrix `X` as I iterate through the chain. I’ve tried several different ways of laying out the `X` object but haven’t found a way that doesn’t lead to a bunch of allocations along in the for loop. Thanks.
```julia
using LinearAlgebra
function update!(X, A)
for k in 2:100
@views mul!(X[:,:,k], X[:,:,k - 1], A)
end
end
function run()
X = ones(2, 2, 100)
A = [0.5 0.5; 0.25 0.75]
@time update!(X, A)
end
run()
```
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### Author:  [@ericphanson](https://discourse.julialang.org/u/ericphanson)
#### Post date: [June 6, 2019, 12:48pm UTC](https://discourse.julialang.org/t/efficiently-calculate-matrices-defined-recursively/25006/2 "2019-06-06T12:48:11Z")
This sounds perfect for a vector of [StaticArrays](https://github.com/JuliaArrays/StaticArrays.jl)! If you need mutability you can use `MArray`‘s, but it will likely be faster by just doing out of place operations on `SArray`‘s.