Add tests from #332

src/problem_depot/problems/lp.jl

@@ -1,4 +1,4 @@
-@add_problem lp function lp_abs_atom(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
+@add_problem lp function lp_dual_abs_atom(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
     x = Variable()
     p = minimize(abs(x), x<=-1)
     if test
@@ -19,6 +19,11 @@
     if test
         @test p.optval ≈ 2 atol=atol rtol=rtol
         @test evaluate(sum(abs(x))) ≈ 2 atol=atol rtol=rtol
+        @test p.constraints[1].dual ≈ 1 atol=atol rtol=rtol
+        @test p.constraints[2].dual ≈ 1 atol=atol rtol=rtol
+        @test p.constraints[3].dual[1,1] ≈ 0 atol=atol rtol=rtol
+        @test p.constraints[3].dual[2,2] ≈ 0 atol=atol rtol=rtol
+        @test p.constraints[3].dual[1,2] ≈ p.constraints[3].dual[2,1] atol=atol rtol=rtol
     end
 end
 
@@ -207,7 +212,7 @@ end
     # TODO: @davidlizeng. We should finish this someday.
 end
 
-@add_problem lp function lp_norm_inf_atom(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
+@add_problem lp function lp_dual_norm_inf_atom(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
     x = Variable(3)
     p = minimize(norm_inf(x), [-2 <= x, x <= 1])
     if test
@@ -217,10 +222,12 @@ end
     if test
         @test p.optval ≈ 0 atol=atol rtol=rtol
         @test evaluate(norm_inf(x)) ≈ 0 atol=atol rtol=rtol
+        @test norm(p.constraints[1].dual) ≈ 0 atol=atol rtol=rtol
+        @test norm(p.constraints[2].dual) ≈ 0 atol=atol rtol=rtol
     end
 end
 
-@add_problem lp function lp_norm_1_atom(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
+@add_problem lp function lp_dual_norm_1_atom(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
     x = Variable(3)
     p = minimize(norm_1(x), [-2 <= x, x <= 1])
     if test
@@ -230,5 +237,7 @@ end
     if test
         @test p.optval ≈ 0 atol=atol rtol=rtol
         @test evaluate(norm_1(x)) ≈ 0 atol=atol rtol=rtol
+        @test norm(p.constraints[1].dual) ≈ 0 atol=atol rtol=rtol
+        @test norm(p.constraints[2].dual) ≈ 0 atol=atol rtol=rtol
     end
 end

src/problem_depot/problems/socp.jl

@@ -370,3 +370,55 @@ end
         @test o1 <= o2
     end
 end
+
+
+@add_problem socp function socp_dual_minimal_norm_solutions(handle_problem!, ::Val{test}, atol, rtol, ::Type{T}) where {T, test}
+    x = Variable(2)
+    A = [1 2; 2 4];
+    b = [3, 6];
+    p = minimize(norm(x, 1), A*x==b)
+
+    if test
+        @test vexity(p) == ConvexVexity()
+    end
+
+    handle_problem!(p)
+    if test
+        @test p.optval ≈ 1.5 atol=atol rtol=rtol
+        @test evaluate(x) ≈ [0, 1.5] atol=atol rtol=rtol
+        @test evaluate(norm(x, 1)) ≈ p.optval atol=atol rtol=rtol
+        @test dot(b, p.constraints[1].dual) ≈ p.optval atol=atol rtol=rtol
+    end
+
+    x = Variable(2)
+    A = [1 2; 2 4];
+    b = [3, 6];
+    p = minimize(norm(x, 2), A*x==b)
+
+    test && @test vexity(p) == ConvexVexity()
+
+    handle_problem!(p)
+
+    if test
+        @test p.optval ≈ 3/sqrt(5) atol=atol rtol=rtol
+        @test evaluate(x) ≈ [3/5, 6/5] atol=atol rtol=rtol
+        @test evaluate(norm(x, 2)) ≈ p.optval atol=atol rtol=rtol
+        @test dot(b, p.constraints[1].dual) ≈ p.optval atol=atol rtol=rtol
+    end
+
+    x = Variable(2)
+    A = [1 2; 2 4];
+    b = [3, 6];
+    p = minimize(norm(x, Inf), A*x==b)
+
+    test && @test vexity(p) == ConvexVexity()
+
+    handle_problem!(p)
+
+    if test
+        @test p.optval ≈ 1.0 atol=atol rtol=rtol
+        @test evaluate(x) ≈ [1, 1] atol=atol rtol=rtol
+        @test evaluate(norm(x, Inf)) ≈ p.optval atol=atol rtol=rtol
+        @test dot(b, p.constraints[1].dual) ≈ p.optval atol=atol rtol=rtol
+    end
+end

test/runtests.jl

@@ -36,7 +36,7 @@ end
     end
 
     @testset "GLPK MIP" begin
-        run_tests(; exclude=[r"socp", r"sdp", r"exp"]) do p
+        run_tests(; exclude=[r"socp", r"sdp", r"exp", r"dual"]) do p
             solve!(p, GLPKSolverMIP())
         end
     end