Skip to content

Latest commit

 

History

History
148 lines (120 loc) · 6.07 KB

README.md

File metadata and controls

148 lines (120 loc) · 6.07 KB
Raw

SCS

Build Status Build status Coverage Status

SCS SCS

Julia wrapper for the SCS splitting cone solver. SCS can solve linear programs, second-order cone programs, semidefinite programs, exponential cone programs, and power cone programs.

Installation

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

julia> Pkg.add("SCS")

SCS.jl will automatically setup the SCS 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.

Usage

MathProgBase wrapper

SCS implements the solver-independent MathProgBase interface, and so can be used within modeling software like Convex and JuMP. The solver object is called SCSSolver. This interface can be used to solve LPs using loadproblem! and SOCPs, SDPs and exponential-cone programs through loadconicproblem!(documentation).

Options

All SCS solver options can be set through the direct interface(documented below) and through MathProgBase. The list of options is defined the scs.h header, which we reproduce here:

max_iters       # maximum iterations to take: 2500 (default)
eps             # convergence tolerance: 1e-3 (default)
alpha           # relaxation parameter: 1.8 (default)
rho_x           # x equality constraint scaling: 1e-3 (default)
cg_rate         # for indirect, tolerance goes down like (1/iter)^cg_rate: 2 (default)
verbose         # boolean, write out progress: 1 (default)
normalize       # boolean, heuristic data rescaling: 1 (default)
scale           # if normalized, rescales by this factor: 5 (default)
warm_start      # boolean, warm start (put initial guess in Sol struct): 0 (default)

To use these settings you can either pass them as keyword arguments to SCS_solve (high level interface) or as arguments to the SCSSolver constructor (MathProgBase interface), e.g.

# Direct
solution = SCS_solve(m, n, A, ..., psize; max_iters=10, verbose=0);
# MathProgBase (with Convex)
m = solve!(problem, SCSSolver(max_iters=10, verbose=0))

High level wrapper

The file high_level_wrapper.jl is thoroughly commented. Here is the basic usage

We assume we are solving a problem of the form

minimize        c' * x
subject to      A * x + s = b
                s in K

where K is a product cone of

  • zero cones,
  • linear cones { x | x >= 0 },
  • second-order cones { (t,x) | ||x||_2 <= t },
  • semi-definite cones { X | X psd },
  • exponential cones {(x,y,z) | y e^(x/y) <= z, y>0 }, and
  • power cone {(x,y,z) | x^a * y^(1-a) >= |z|, x>=0, y>=0}.

The problem data are

  • A is the matrix with m rows and n cols
  • b is of length m x 1
  • c is of length n x 1
  • f is the number of primal zero / dual free cones, i.e. primal equality constraints
  • l is the number of linear cones
  • q is the array of SOCs sizes
  • s is the array of SDCs sizes
  • ep is the number of primal exponential cones
  • ed is the number of dual exponential cones
  • p is the array of power cone parameters
  • options is a dictionary of options (see below).

The function is

function SCS_solve(m::Int, n::Int, A::SCSVecOrMatOrSparse, b::Array{Float64,},
    c::Array{Float64,}, f::Int, l::Int, q::Array{Int,}, qsize::Int, s::Array{Int,},
    ssize::Int, ep::Int, ed::Int, p::Array{Float64,}, psize::Int; options...)

and it returns an object of type Solution, which contains the following fields

type Solution
  x::Array{Float64, 1}
  y::Array{Float64, 1}
  s::Array{Float64, 1}
  status::ASCIIString
  ret_val::Int
  ...

Where x stores the optimal value of the primal variable, y stores the optimal value of the dual variable, s is the slack variable, status gives information such as solved, primal infeasible, etc.

Low level wrapper

The low level wrapper directly calls SCS and is also thoroughly documented in low_level_wrapper.jl. The low level wrapper performs the pointer manipulation necessary for the direct C call.

Convex and JuMP examples

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

using Convex, SCS
items  = [:Gold, :Silver, :Bronze]
values = [5.0, 3.0, 1.0]
weights = [2.0, 1.5, 0.3]

# Define a variable of size 3, each index representing an item
x = Variable(3)
p = maximize(x' * values, 0 <= x, x <= 1, x' * weights <= 3)
solve!(p, SCSSolver())
println([items x.value])

# [:Gold 0.9999971880377178
#  :Silver 0.46667637765641057
#  :Bronze 0.9999998036351865]

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

using JuMP, SCS
items  = [:Gold, :Silver, :Bronze]
values = [:Gold => 5.0,  :Silver => 3.0,  :Bronze => 1.0]
weight = [:Gold => 2.0,  :Silver => 1.5,  :Bronze => 0.3]

m = Model(solver=SCSSolver())
@defVar(m, 0 <= take[items] <= 1)  # Define a variable for each item
@setObjective(m, Max, sum{ values[item] * take[item], item in items})
@addConstraint(m, sum{ weight[item] * take[item], item in items} <= 3)
solve(m)
println(getValue(take))
# [Bronze] = 0.9999999496295456
# [  Gold] = 0.99999492720597
# [Silver] = 0.4666851698368782