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Julia wrapper for the ECOS embeddable conic optimization interior point solver.

You can install ECOS.jl through the Julia package manager:

```
julia> Pkg.add("ECOS")
```

ECOS.jl will automatically setup the ECOS solver itself:

- On Linux it will build from source
- On OS X it will download a binary via Homebrew.jl.
- On Windows it will download a binary.

The ECOS interface is completely wrapped. ECOS functions corresponding to the C API are available as `ECOS.setup`

, `ECOS.solve`

, `ECOS.cleanup`

, and `ECOS.ver`

(these are not exported from the module). Function arguments are extensively documented in the source, and an example of usage can be found in `test/direct.jl`

.

ECOS.jl also supports the JuliaOpt **MathProgBase** standard solver interface.
Thanks to this support ECOS can be used as a solver with both the **JuMP** and **Convex.jl** modeling languages.

All ECOS solver options can be set through the direct interface and through MathProgBase.
The list of options is defined the `ecos.h`

header, which we reproduce here:

```
gamma # scaling the final step length
delta # regularization parameter
eps # regularization threshold
feastol # primal/dual infeasibility tolerance
abstol # absolute tolerance on duality gap
reltol # relative tolerance on duality gap
feastol_inacc # primal/dual infeasibility relaxed tolerance
abstol_inacc # absolute relaxed tolerance on duality gap
reltol_inacc # relative relaxed tolerance on duality gap
nitref # number of iterative refinement steps
maxit # maximum number of iterations
verbose # verbosity bool for PRINTLEVEL < 3
```

To use these settings you can either pass them as keyword arguments to `setup`

(direct interface) or as arguments to the `ECOSSolver`

constructor (MathProgBase interface), e.g.

```
# Direct
my_prob = ECOS.setup(n, m, ..., c, h, b; maxit=10, feastol=1e-5)
# MathProgBase (with JuMP)
m = Model(solver=ECOS.ECOSSolver(maxit=10, feastol=1e-5))
```

This example shows how we can model a simple knapsack problem with JuMP and use ECOS to solve it.

```
using JuMP
using ECOS
items = [:Gold, :Silver, :Bronze]
values = Dict(:Gold => 5.0, :Silver => 3.0, :Bronze => 1.0)
weight = Dict(:Gold => 2.0, :Silver => 1.5, :Bronze => 0.3)
m = Model(solver=ECOSSolver())
@variable(m, 0 <= take[items] <= 1) # Define a variable for each item
@objective(m, Max, sum{ values[item] * take[item], item in items})
@constraint(m, sum{ weight[item] * take[item], item in items} <= 3)
solve(m)
println(getvalue(take))
# take
# [ Gold] = 0.9999999680446406
# [Silver] = 0.46666670881026834
# [Bronze] = 0.9999999633898735
```

`ECOS.jl`

is licensed under the MIT License (see LICENSE.md), but note that ECOS itself is GPL v3.

02/20/2014

2 months ago

155 commits