Pg/one big module

.travis.yml

@@ -27,9 +27,9 @@ git:
 
 ## uncomment the following lines to override the default test script
 #script:
-#  - julia -e 'Pkg.clone(pwd()); Pkg.build("QOCS"); Pkg.test("QOCS"; coverage=true)'
+#  - julia -e 'Pkg.clone(pwd()); Pkg.build("COSMO"); Pkg.test("COSMO"; coverage=true)'
 after_success:
   # push coverage results to Coveralls
-  - julia -e 'cd(Pkg.dir("QOCS")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
+  - julia -e 'cd(Pkg.dir("COSMO")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
   # push coverage results to Codecov
-  - julia -e 'cd(Pkg.dir("QOCS")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())'
+  - julia -e 'cd(Pkg.dir("COSMO")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())'

README.md

@@ -1,21 +1,21 @@
-![QOCS Logo](https://github.com/migarstka/QOCS_assets/blob/master/QOCS_logo.png)
-This repository hosts a Julia implementation of the QOCS solver. It solves convex optimization problems of the following form:
+![COSMO Logo](https://github.com/migarstka/COSMO_assets/blob/master/COSMO_logo.png)
+This repository hosts a Julia implementation of the COSMO solver. It solves convex optimization problems of the following form:
 ```
 min 1/2 x'Px + q'x
 s.t. Ax + s = b, s in C
 ```
-with decision variables `x ϵ R^n`, `s ϵ R^m` and data matrices `P=P'>=0`, `q ϵ R^n`, `A ϵ R^(m×n)`, and `b ϵ R^m`. The convex set C is a composition of convex sets and cones. By default QOCS supports the zero cone, the non-negative orthant, second order cones and positive semidefinite cones. Further convex sets can be added by the user.
+with decision variables `x ϵ R^n`, `s ϵ R^m` and data matrices `P=P'>=0`, `q ϵ R^n`, `A ϵ R^(m×n)`, and `b ϵ R^m`. The convex set C is a composition of convex sets and cones. By default COSMO supports the zero cone, the non-negative orthant, second order cones and positive semidefinite cones. Further convex sets can be added by the user.
 
 ## Installation / Usage
 - The Solver was written for Julia v0.7/1.0
-- Clone package to local machine: `Pkg.clone("https://github.com/oxfordcontrol/QOCS.jl")`
-- Load the package with `using QOCS`
+- Clone package to local machine: `Pkg.clone("https://github.com/oxfordcontrol/COSMO.jl")`
+- Load the package with `using COSMO`
 - Consider the following example:
 
 ### Example
 ```julia
 
-using QOCS, Test, LinearAlgebra
+using COSMO, Test, LinearAlgebra
 
 # Linear program example
 # min c'x
@@ -30,15 +30,15 @@ Aa = -[A;-Matrix(1.0I,4,4);0 -1 0 0;-1 0 -1 0]
 ba = vec([b; -ones(4,1);-5;-4])
 P = zeros(size(A,2),size(A,2))
 
-constraint1 = QOCS.Constraint(Aa,ba,QOCS.Nonnegatives())
+constraint1 = COSMO.Constraint(Aa,ba,COSMO.Nonnegatives)
 
 # define example problem
-settings = QOCS.Settings(rho=0.1,sigma=1e-6,alpha=1.6,max_iter=2500,verbose=true,check_termination=1,eps_abs = 1e-6, eps_rel = 1e-6)
+settings = COSMO.Settings(rho=0.1,sigma=1e-6,alpha=1.6,max_iter=2500,verbose=true,check_termination=1,eps_abs = 1e-6, eps_rel = 1e-6)
 
-model = QOCS.Model()
+model = COSMO.Model()
 assemble!(model,P,c,[constraint1])
 
-res = QOCS.optimize!(model,settings);
+res = COSMO.optimize!(model,settings);
 
 @testset "Linear Problem" begin
   @test isapprox(res.x[1:4],[3;5;1;1], atol=1e-2, norm=(x -> norm(x,Inf)))
@@ -49,7 +49,7 @@ nothing
 
 
 ## Settings
-Settings can be specified using the `QOCS.Settings` struct. The following settings are available:
+Settings can be specified using the `COSMO.Settings` struct. The following settings are available:
 
 Argument | Description | Values (default)
 --- | --- | ---
@@ -72,7 +72,7 @@ time_limit | set solver time limit in s | 0
 For more low-level settings, see the Settings definition in `/src/Types.jl`.
 
 ## Result
-After attempting to solve the problem, QOCS will return a result object with the following fields:
+After attempting to solve the problem, COSMO will return a result object with the following fields:
 
 Fieldname | Type | Description
 ---  | --- | ---
@@ -82,11 +82,11 @@ s | Vector{Float64}| (Primal) set variable
 objVal | Float64 | Objective value
 iter | Int64 | Number of iterations
 status | Symbol | Solution status
-info | QOCS.ResultInfo | Struct with more information
-times | QOCS.ResultTimes | Struct with several measured times
+info | COSMO.ResultInfo | Struct with more information
+times | COSMO.ResultTimes | Struct with several measured times
 
 ### Status Codes
-QOCS will return one of the following statuses:
+COSMO will return one of the following statuses:
 
 Status Code  | Description
 ---  | ---
@@ -99,7 +99,7 @@ Status Code  | Description
 
 
 ### Timings
-If `settings.verboseTiming` is set to `true` QOCS will report the following times in `result.times`:
+If `settings.verboseTiming` is set to `true` COSMO will report the following times in `result.times`:
 
 Time Name  | Description
 ---  | ---

appveyor.yml

@@ -41,7 +41,7 @@ build_script:
 # Need to convert from shallow to complete for Pkg.clone to work
   - IF EXIST .git\shallow (git fetch --unshallow)
   - C:\projects\julia\bin\julia -e "versioninfo();
-      Pkg.clone(pwd(), \"QOCS\"); Pkg.build(\"QOCS\")"
+      Pkg.clone(pwd(), \"COSMO\"); Pkg.build(\"COSMO\")"
 
 test_script:
-  - C:\projects\julia\bin\julia -e "Pkg.test(\"QOCS\")"
+  - C:\projects\julia\bin\julia -e "Pkg.test(\"COSMO\")"

examples/lp.jl

@@ -1,7 +1,7 @@
 # Test script to test solver for an lp
 
 using Test
-using QOCS, LinearAlgebra
+using COSMO, LinearAlgebra
 
 # Linear program example
 # min c'x
@@ -16,18 +16,18 @@ Aa = -[A;-Matrix(1.0I,4,4);0 -1 0 0;-1 0 -1 0]
 ba = vec([b; -ones(4,1);-5;-4])
 P = zeros(size(A,2),size(A,2))
 
-constraint1 = QOCS.Constraint(Aa,ba,QOCS.Nonnegatives())
+constraint1 = COSMO.Constraint(Aa,ba,COSMO.Nonnegatives)
 
 # define example problem
-settings = QOCS.Settings(rho=0.1,sigma=1e-6,alpha=1.6,max_iter=2500,verbose=true,check_termination=1,eps_abs = 1e-6, eps_rel = 1e-6)
+settings = COSMO.Settings(rho=0.1,sigma=1e-6,alpha=1.6,max_iter=2500,verbose=true,check_termination=1,eps_abs = 1e-6, eps_rel = 1e-6)
 
-model = QOCS.Model()
+model = COSMO.Model()
 assemble!(model,P,c,constraint1)
 
-res = QOCS.optimize!(model,settings);
+res = COSMO.optimize!(model,settings);
 
 @testset "Linear Problem" begin
   @test isapprox(res.x[1:4],[3;5;1;1], atol=1e-2, norm=(x -> norm(x,Inf)))
   @test isapprox(res.objVal,20.0, atol=1e-2)
 end
-nothing
+nothing

examples/maxEigenvalue.jl

@@ -7,7 +7,7 @@
 # This immediately gives us that
 # λ1 = min{t | s.t. tI − A ≥ 0}
 using Test
-using QOCS, SparseArrays,LinearAlgebra, Random
+using COSMO, SparseArrays,LinearAlgebra, Random
 
 nn = 10
 rng = MersenneTwister(7232)
@@ -26,15 +26,15 @@ rng = MersenneTwister(7232)
     b1 = 1.
     b2 = zeros(r^2)
 
-    constraint1 = QOCS.Constraint(A1,b1,QOCS.Zeros())
-    constraint2 = QOCS.Constraint(A2,b2,QOCS.PositiveSemidefiniteCone())
+    constraint1 = COSMO.Constraint(A1,b1,COSMO.ZeroSet)
+    constraint2 = COSMO.Constraint(A2,b2,COSMO.PsdCone)
     P = spzeros(r^2,r^2)
 
-    settings = QOCS.Settings(check_termination=1,scaling = 0)
+    settings = COSMO.Settings(check_termination=1,scaling = 0)
 
-    model = QOCS.Model()
+    model = COSMO.Model()
     assemble!(model,P,c,[constraint1;constraint2])
-    res = QOCS.optimize!(model,settings);
+    res = COSMO.optimize!(model,settings);
 
 
 
@@ -46,4 +46,3 @@ rng = MersenneTwister(7232)
    end
 end
 nothing
-

examples/qp.jl

@@ -1,7 +1,7 @@
 # Test script to test solver for a qp
 
 using Test
-using QOCS, SparseArrays, LinearAlgebra
+using COSMO, SparseArrays, LinearAlgebra
 
 # Quadratic program example from OSQP Doc
 # min 0.5 * x'Px +  q'x
@@ -15,23 +15,23 @@ u = [1; 0.7; 0.7]
 # Define the constraint l <= Ax <= u with the help of a Nonnegatives set
 Aa = [-A;A]
 ba = [u; -l]
-constraint1 = QOCS.Constraint(Aa,ba,QOCS.Nonnegatives())
+constraint1 = COSMO.Constraint(Aa,ba,COSMO.Nonnegatives)
 
 # define example problem
-settings = QOCS.Settings(verbose=true,eps_abs = 1e-4,eps_rel = 1e-4)
+settings = COSMO.Settings(verbose=true,eps_abs = 1e-4,eps_rel = 1e-4)
 
 
-model = QOCS.Model()
+model = COSMO.Model()
 assemble!(model,P,q,constraint1)
-res = QOCS.optimize!(model,settings);
+res = COSMO.optimize!(model,settings);
 
 # solve again by defining the constraints with the help of a box (disable infeasibility checks)
-constraint1 = QOCS.Constraint(A,zeros(3),QOCS.Box(l,u))
-settings = QOCS.Settings(check_infeasibility = 2500, verbose=true,eps_abs = 1e-4,eps_rel = 1e-4)
+constraint1 = COSMO.Constraint(A,zeros(3),COSMO.Box(l,u))
+settings = COSMO.Settings(check_infeasibility = 2500, verbose=true,eps_abs = 1e-4,eps_rel = 1e-4)
 
-model = QOCS.Model()
+model = COSMO.Model()
 assemble!(model,P,q,constraint1)
-res_box = QOCS.optimize!(model,settings);
+res_box = COSMO.optimize!(model,settings);
 
 
 @testset "QP Problem" begin

src/COSMO.jl

@@ -0,0 +1,32 @@
+#__precompile__()
+module COSMO
+
+using SparseArrays,LinearAlgebra, SuiteSparse
+
+#export MathOptInterfaceCOSMO
+export  assemble!,
+        optimize!,
+        warmStart!,
+        reset!
+
+const DefaultFloat = Float64
+const DefaultInt   = Int64
+
+
+
+include("./algebra.jl")
+include("./projections.jl")
+include("./types.jl")               # some types still need tidying
+include("./settings.jl")            # TODO: unmodified - revisit
+include("./constraint.jl")          # TODO: unmodified - revisit
+include("./parameters.jl")          # TODO: unmodified - revisit
+include("./residuals.jl")           # TODO: unmodified - revisit
+include("./scaling.jl")             # TODO: set scaling / E scaling is broken
+include("./kkt.jl")                 # TODO: unmodified - revisit.  Add lin solver type
+include("./infeasibility.jl")       # TODO: stylistic fixes needed
+include("./printing.jl")            # TODO: unmodified - revisit
+include("./setup.jl")               # TODO: unmodified - revisit (short - consolidate?)
+include("./solver.jl")              # TODO: unmodified - revisit
+include("./interface.jl")           # TODO: unmodified - revisit
+
+end #end module

src/Helper.jl → src/COSMOTestUtils.jl

@@ -1,4 +1,4 @@
-module Helper
+module COSMOTestUtils
 using LinearAlgebra, Random
 export generatePosDefMatrix, isNumericallyPosSemDef, isNumericallySymmetric, findNonSymmetricComponent, findDifferentElements, reCreateSparseMatrix,duplicateSparsityPattern, gmean
 
@@ -108,4 +108,4 @@ function gmean(a::AbstractArray{T}) where T<:Real
     end
     return exp(s / n)
 end
-end #MODULE
+end #MODULE