Update URLs for jump-dev migration

Author: mlubin

LICENSE.md

@@ -25,7 +25,7 @@ The Convex.jl package is licensed under the Simplified "2-clause" BSD License:
 > OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 The file src/problem_depot/problem_depot.jl contains some utilities copied from
-the MathOptInterface.jl package (https://github.com/JuliaOpt/MathOptInterface.jl)
+the MathOptInterface.jl package (https://github.com/jump-dev/MathOptInterface.jl)
 which is licensed under the following MIT License:
 
 >Copyright (c) 2017: Miles Lubin and contributors Copyright (c) 2017: Google Inc.

README.md

@@ -1,16 +1,16 @@
 # Convex.jl
 
-[![Build Status](https://travis-ci.org/JuliaOpt/Convex.jl.svg?branch=master)](https://travis-ci.org/JuliaOpt/Convex.jl)
-[![Coverage Status](https://coveralls.io/repos/JuliaOpt/Convex.jl/badge.svg?branch=master)](https://coveralls.io/r/JuliaOpt/Convex.jl?branch=master)
-[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.juliaopt.org/Convex.jl/stable)
-[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://www.juliaopt.org/Convex.jl/dev)
+[![Build Status](https://travis-ci.org/jump-dev/Convex.jl.svg?branch=master)](https://travis-ci.org/jump-dev/Convex.jl)
+[![Coverage Status](https://coveralls.io/repos/jump-dev/Convex.jl/badge.svg?branch=master)](https://coveralls.io/r/jump-dev/Convex.jl?branch=master)
+[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://jump.dev/Convex.jl/stable)
+[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://jump.dev/Convex.jl/dev)
 
-**Convex.jl** is a [Julia](http://julialang.org) package for [Disciplined Convex Programming](http://dcp.stanford.edu/). Convex.jl can solve linear programs, mixed-integer linear programs, and DCP-compliant convex programs using a variety of solvers, including [Mosek](https://github.com/JuliaOpt/Mosek.jl), [Gurobi](https://github.com/JuliaOpt/Gurobi.jl), [ECOS](https://github.com/JuliaOpt/ECOS.jl), [SCS](https://github.com/JuliaOpt/SCS.jl), and  [GLPK](https://github.com/JuliaOpt/GLPK.jl), through [MathOptInterface](https://github.com/JuliaOpt/MathOptInterface.jl). It also supports optimization with complex variables and coefficients.
+**Convex.jl** is a [Julia](http://julialang.org) package for [Disciplined Convex Programming](http://dcp.stanford.edu/). Convex.jl can solve linear programs, mixed-integer linear programs, and DCP-compliant convex programs using a variety of solvers, including [Mosek](https://github.com/JuliaOpt/Mosek.jl), [Gurobi](https://github.com/jump-dev/Gurobi.jl), [ECOS](https://github.com/jump-dev/ECOS.jl), [SCS](https://github.com/jump-dev/SCS.jl), and  [GLPK](https://github.com/JuliaOpt/GLPK.jl), through [MathOptInterface](https://github.com/jump-dev/MathOptInterface.jl). It also supports optimization with complex variables and coefficients.
 
 **Installation**: `julia> Pkg.add("Convex")`
 
-- **Detailed documentation and examples** for Convex.jl ([stable](https://juliaopt.github.io/Convex.jl/stable) | [development version](https://juliaopt.github.io/Convex.jl/dev)).
-- If you're running into **bugs or have feature requests**, please use the [Github Issue Tracker](https://github.com/JuliaOpt/Convex.jl/issues>).
+- **Detailed documentation and examples** for Convex.jl ([stable](https://jump.dev/Convex.jl/stable) | [development version](https://jump.dev/Convex.jl/dev)).
+- If you're running into **bugs or have feature requests**, please use the [Github Issue Tracker](https://github.com/jump-dev/Convex.jl/issues>).
 - For usage questions, please contact us via [Discourse](https://discourse.julialang.org/c/domain/opt).
 
 ## Quick Example
@@ -49,10 +49,10 @@ problem.optval
 
 ## More Examples
 
-A number of examples can be found [here](https://www.juliaopt.org/Convex.jl/stable/).
-The [basic usage notebook](https://www.juliaopt.org/Convex.jl/stable/examples/general_examples/basic_usage/)
+A number of examples can be found [here](https://jump.dev/Convex.jl/stable/).
+The [basic usage notebook](https://jump.dev/Convex.jl/stable/examples/general_examples/basic_usage/)
 gives a simple tutorial on problems that can be solved using Convex.jl. All examples can be downloaded as
-a zip file from [here](https://www.juliaopt.org/Convex.jl/stable/examples/notebooks.zip).
+a zip file from [here](https://jump.dev/Convex.jl/stable/examples/notebooks.zip).
 
 ## Citing this package
 

docs/make.jl

@@ -146,10 +146,10 @@ makedocs(;
         "Credits" => "credits.md",
         "Examples" => examples_nav,
     ],
-    repo = "https://github.com/JuliaOpt/Convex.jl/blob/{commit}{path}#L{line}",
+    repo = "https://github.com/jump-dev/Convex.jl/blob/{commit}{path}#L{line}",
     sitename = "Convex.jl")
 
-deploydocs(repo = "github.com/JuliaOpt/Convex.jl.git", push_preview = true)
+deploydocs(repo = "github.com/jump-dev/Convex.jl.git", push_preview = true)
 
 # restore the environmental variable `GKSwstype`.
 ENV["GKSwstype"] = previous_GKSwstype;

docs/src/contributing.md

@@ -6,10 +6,10 @@ short instructions on how to get started. If you don't know what you'd
 like to contribute, you could
 
  -   take a look at the current
-     [issues](https://github.com/JuliaOpt/Convex.jl/issues) and pick
+     [issues](https://github.com/jump-dev/Convex.jl/issues) and pick
      one. (Feature requests are probably the easiest to tackle.)
  -   add a [usage
-     example](https://github.com/JuliaOpt/Convex.jl/tree/master/examples).
+     example](https://github.com/jump-dev/Convex.jl/tree/master/examples).
 
 Then submit a pull request (PR). (Let us know if it's a work in
 progress by putting \[WIP\] in the name of the PR.)
@@ -18,7 +18,7 @@ Adding examples
 ---------------
 
  -   Take a look at our exising [usage
-     examples](https://github.com/JuliaOpt/Convex.jl/tree/master/examples)
+     examples](https://github.com/jump-dev/Convex.jl/tree/master/examples)
      and add another in similar style.
  -   Submit a PR. (Let us know if it's a work in progress by putting
      \[WIP\] in the name of the PR.)
@@ -32,9 +32,9 @@ Here are the steps to add a new function or operation (atom) to
 Convex.jl. Let's say you're adding the new function $f$.
 
  -   Take a look at the [nuclear norm
-     atom](https://github.com/JuliaOpt/Convex.jl/blob/master/src/atoms/sdp_cone/nuclearnorm.jl)
+     atom](https://github.com/jump-dev/Convex.jl/blob/master/src/atoms/sdp_cone/nuclearnorm.jl)
      for an example of how to construct atoms, and see the [norm
-     atom](https://github.com/JuliaOpt/Convex.jl/blob/master/src/atoms/second_order_cone/norm.jl)
+     atom](https://github.com/jump-dev/Convex.jl/blob/master/src/atoms/second_order_cone/norm.jl)
      for an example of an atom that depends on a parameter.
  -   Copy paste (eg) the nuclear norm file, replace anything saying
      nuclear norm with the name of the atom $f$, fill in monotonicity,
@@ -72,15 +72,15 @@ Then read the conic form code:
 
  -   We define data structures for conic objectives and conic
      constraints, and simple ways of combining them, in
-     [conic\_form.jl](https://github.com/JuliaOpt/Convex.jl/blob/master/src/conic_form.jl)
+     [conic\_form.jl](https://github.com/jump-dev/Convex.jl/blob/master/src/conic_form.jl)
  -   We load the internal conic form representation into the
-     [MathOptInterface](https://github.com/JuliaOpt/MathOptInterface.jl)
+     [MathOptInterface](https://github.com/jump-dev/MathOptInterface.jl)
      model in the function
-     [load\_MOI\_model!](https://github.com/JuliaOpt/Convex.jl/blob/master/src/solution.jl#L151).
+     [load\_MOI\_model!](https://github.com/jump-dev/Convex.jl/blob/master/src/solution.jl#L151).
  -   We solve problems (that is, pass the standard form of the problem
      to a solver, and put the solution back into the values of the
      appropriate variables) in
-     [solve!](https://github.com/JuliaOpt/Convex.jl/blob/master/src/solution.jl#L205).
+     [solve!](https://github.com/jump-dev/Convex.jl/blob/master/src/solution.jl#L205).
 
 You're now armed and dangerous. Go ahead and open an issue (or comment
 on a previous one) if you can't figure something out, or submit a PR if

src/Convex.jl

@@ -165,9 +165,9 @@ include("utilities/iteration.jl")
 include("utilities/broadcast.jl")
 include("problem_depot/problem_depot.jl")
 
-# Deprecated workaround for memory leak (https://github.com/JuliaOpt/Convex.jl/issues/83)
+# Deprecated workaround for memory leak (https://github.com/jump-dev/Convex.jl/issues/83)
 function clearmemory()
-    Base.depwarn("Convex.clearmemory() is deprecated, as the memory leak it works around has been closed (in https://github.com/JuliaOpt/Convex.jl/pull/322). This function no longer does anything and will be removed in a future Convex.jl release.", :clearmemory )
+    Base.depwarn("Convex.clearmemory() is deprecated, as the memory leak it works around has been closed (in https://github.com/jump-dev/Convex.jl/pull/322). This function no longer does anything and will be removed in a future Convex.jl release.", :clearmemory )
 end
 
 @deprecate lambdamin eigmin

src/atoms/second_order_cone/power_to_socp.jl

@@ -10,7 +10,7 @@
 # norm([2x, t - s]) <= t + s, t >= 0, s >= 0.
 #
 # This reduction is documented in the pdf available at
-# https://github.com/JuliaOpt/Convex.jl/raw/master/docs/supplementary/rational_to_socp.pdf
+# https://github.com/jump-dev/Convex.jl/raw/master/docs/supplementary/rational_to_socp.pdf
 
 module psocp
 

src/atoms/second_order_cone/rationalnorm.jl

@@ -8,7 +8,7 @@
 # this is based on the paper "Second-order cone programming" by
 # F. Alizadeh and D. Goldfarb, Mathematical Programming, Series B,
 # 95:3-51, 2001, and is documented in the pdf available at
-# https://github.com/JuliaOpt/Convex.jl/raw/master/docs/supplementary/rational_to_socp.pdf
+# https://github.com/jump-dev/Convex.jl/raw/master/docs/supplementary/rational_to_socp.pdf
 #############################################################################
 
 ### k-norm for rational k

src/problem_depot/problems/sdp.jl

@@ -124,7 +124,7 @@ end
         @test evaluate(eigmax(y)) ≈ 4 atol=atol rtol=rtol
     end
 
-    # https://github.com/JuliaOpt/Convex.jl/issues/337
+    # https://github.com/jump-dev/Convex.jl/issues/337
     x = ComplexVariable(2, 2)
     p = minimize( eigmax(x), [ x[1,2] == im, x[2,2] == 1.0, x ⪰ - eye(2) ]; numeric_type = T)
     handle_problem!(p)

test/runtests.jl

@@ -35,7 +35,7 @@ end
     include("deprecations.jl")
 
     @testset "SCS with warmstarts" begin
-        # We exclude `sdp_sdp_constraints` since it seems to hit a bug https://github.com/JuliaOpt/SCS.jl/issues/167
+        # We exclude `sdp_sdp_constraints` since it seems to hit a bug https://github.com/jump-dev/SCS.jl/issues/167
         run_tests(; exclude=[r"mip", r"sdp_sdp_constraints"]) do p
             solve!(p, () -> SCS.Optimizer(verbose=0, eps=1e-6); warmstart = true)
         end