# LSODA.jl: the LSODA algorithm for solving ordinary differential equations

## Introduction

LSODA.jl is a Julia package that interfaces to the liblsoda library, developped by Simon Frost (@sdwfrost), thereby providing a way to use the LSODA algorithm from Linda Petzold and Alan Hindmarsh from Julia. Clang.jl has been used to write the library and Sundials.jl was a inspiring source.

## Installation

To install this package, run the command Pkg.clone("https://github.com/rveltz/LSODA.jl.git")

## Example

We first need to load the library.

example 1 using LSODA

We next need to define a function that provides the derivatives ydot given the time, t, initial conditions, y, and optionally some additional data, data. Note that this function modifies ydot in-place and returns nothing.

example 1
function rhs!(t, y, ydot, data)
ydot=1.0E4 * y * y - .04E0 * y
ydot=3.0E7 * y * y
ydot=-ydot - ydot
nothing
end

The model can be solved by providing an initial condition for the state variables, and a time span over which to simulate.

example 1 y0 = [1.,0.,0.] tspan = [0., 0.4] res = lsoda(rhs!, y0, tspan, reltol= 1e-4, abstol = Vector([1.e-6,1.e-10,1.e-6]))

This should give the following.

example 1
at t =   4.0000e-01 y=   9.851712e-01   3.386380e-05   1.479493e-02
at t =   4.0000e+00 y=   9.055333e-01   2.240655e-05   9.444430e-02
at t =   4.0000e+01 y=   7.158403e-01   9.186334e-06   2.841505e-01
at t =   4.0000e+02 y=   4.505250e-01   3.222964e-06   5.494717e-01
at t =   4.0000e+03 y=   1.831976e-01   8.941774e-07   8.168016e-01
at t =   4.0000e+04 y=   3.898729e-02   1.621940e-07   9.610125e-01
at t =   4.0000e+05 y=   4.936362e-03   1.984221e-08   9.950636e-01
at t =   4.0000e+06 y=   5.161832e-04   2.065786e-09   9.994838e-01
at t =   4.0000e+07 y=   5.179811e-05   2.072030e-10   9.999482e-01
at t =   4.0000e+08 y=   5.283524e-06   2.113420e-11   9.999947e-01
at t =   4.0000e+09 y=   4.658945e-07   1.863579e-12   9.999995e-01
at t =   4.0000e+10 y=   1.423392e-08   5.693574e-14   1.000000e+00

## Application programming interface

### Functions

# LSODA.lsodaType.

lsoda(f::Function, y0::Vector{Float64}, tspan::Vector{Float64}; userdata::Any=nothing, reltol::Union{Float64,Vector}=1e-4, abstol::Union{Float64,Vector}=1e-10)

Solves a set of ordinary differential equations using the LSODA algorithm. The vector field encoded in an inplace f::Function needs to have the self-explanatory arguments f(t, y, ydot, data)

# LSODA.lsoda_evolve!Function.

lsoda_evolve!(ctx::lsoda_context_t,y::Vector{Float64},tspan::Vector{Float64})

Solves a set of ordinary differential equations using the LSODA algorithm and the context variable ctx. This avoid re-allocating ctx. You have to be carefull to remember the current time or this function will return an error.