Add tests

Author: SophieL1

test/ArrayDiff.jl

@@ -40,6 +40,25 @@ function test_objective_dot_univariate()
     return
 end
 
+function test_objective_dot_univariate_and_scalar_mult()
+    model = Nonlinear.Model()
+    x = MOI.VariableIndex(1)
+    Nonlinear.set_objective(model, :(2*(dot([$x], [$x]))))
+    evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x])
+    MOI.initialize(evaluator, [:Grad])
+    sizes = evaluator.backend.objective.expr.sizes
+    @test sizes.ndims == [0, 0, 0, 1, 0, 1, 0]
+    @test sizes.size_offset == [0, 0, 0, 1, 0, 0, 0]
+    @test sizes.size == [1, 1]
+    @test sizes.storage_offset == [0, 1, 2, 3, 4, 5, 6, 7]
+    x = [1.2]
+    @test MOI.eval_objective(evaluator, x) == 2*x[1]^2
+    g = ones(1)
+    MOI.eval_objective_gradient(evaluator, g, x)
+    @test g[1] == 4x[1]
+    return
+end
+
 function test_objective_dot_bivariate()
     model = Nonlinear.Model()
     x = MOI.VariableIndex(1)
@@ -280,62 +299,62 @@ function test_objective_norm_of_row()
     return
 end
 
-#function test_objective_norm_of_matrix()
-#    model = Nonlinear.Model()
-#    x1 = MOI.VariableIndex(1)
-#    x2 = MOI.VariableIndex(2)
-#    x3 = MOI.VariableIndex(3)
-#    x4 = MOI.VariableIndex(4)
-#    Nonlinear.set_objective(model, :(norm([$x1 $x2; $x3 $x4])))
-#    evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x1, x2, x3, x4])
-#    MOI.initialize(evaluator, [:Grad])
-#    sizes = evaluator.backend.objective.expr.sizes
-#    @test sizes.ndims == [0, 2, 2, 0, 0, 2, 0, 0]
-#    @test sizes.size_offset == [0, 4, 2, 0, 0, 0, 0, 0]
-#    @test sizes.size == [1, 2, 1, 2, 2, 2]
-#    @test sizes.storage_offset == [0, 1, 5, 7, 8, 9, 11, 12, 13]
-#    x1 = 1.0
-#    x2 = 2.0
-#    x3 = 3.0
-#    x4 = 4.0
-#    @test MOI.eval_objective(evaluator, [x1, x2, x3, x4]) == sqrt(30.0)
-#    g = ones(4)
-#    MOI.eval_objective_gradient(evaluator, g, [x1, x2, x3, x4])
-#    @test g == [
-#        1.0 / sqrt(30.0),
-#        2.0 / sqrt(30.0),
-#        3.0 / sqrt(30.0),
-#        4.0 / sqrt(30.0),
-#    ]
-#    return
-#end
-#
-#function test_objective_norm_of_matrix_with_sum()
-#    model = Nonlinear.Model()
-#    x1 = MOI.VariableIndex(1)
-#    x2 = MOI.VariableIndex(2)
-#    x3 = MOI.VariableIndex(3)
-#    x4 = MOI.VariableIndex(4)
-#    Nonlinear.set_objective(model, :(norm([$x1 $x2; $x3 $x4] - [1 1; 1 1])))
-#    evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x1, x2, x3, x4])
-#    MOI.initialize(evaluator, [:Grad])
-#    sizes = evaluator.backend.objective.expr.sizes
-#    @test sizes.ndims == [0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0]
-#    @test sizes.size_offset ==
-#          [0, 12, 10, 8, 0, 0, 6, 0, 0, 4, 2, 0, 0, 0, 0, 0]
-#    @test sizes.size == [1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2]
-#    @test sizes.storage_offset ==
-#          [0, 1, 5, 9, 11, 12, 13, 15, 16, 17, 21, 23, 24, 25, 27, 28, 29]
-#    x1 = 1.0
-#    x2 = 2.0
-#    x3 = 3.0
-#    x4 = 4.0
-#    @test MOI.eval_objective(evaluator, [x1, x2, x3, x4]) == sqrt(14.0)
-#    g = ones(4)
-#    MOI.eval_objective_gradient(evaluator, g, [x1, x2, x3, x4])
-#    @test g == [0.0, 1.0 / sqrt(14.0), 2.0 / sqrt(14.0), 3.0 / sqrt(14.0)]
-#    return
-#end
+function test_objective_norm_of_matrix()
+    model = Nonlinear.Model()
+    x1 = MOI.VariableIndex(1)
+    x2 = MOI.VariableIndex(2)
+    x3 = MOI.VariableIndex(3)
+    x4 = MOI.VariableIndex(4)
+    Nonlinear.set_objective(model, :(norm([$x1 $x2; $x3 $x4])))
+    evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x1, x2, x3, x4])
+    MOI.initialize(evaluator, [:Grad])
+    sizes = evaluator.backend.objective.expr.sizes
+    @test sizes.ndims == [0, 2, 2, 0, 0, 2, 0, 0]
+    @test sizes.size_offset == [0, 4, 2, 0, 0, 0, 0, 0]
+    @test sizes.size == [1, 2, 1, 2, 2, 2]
+    @test sizes.storage_offset == [0, 1, 5, 7, 8, 9, 11, 12, 13]
+    x1 = 1.0
+    x2 = 2.0
+    x3 = 3.0
+    x4 = 4.0
+    @test MOI.eval_objective(evaluator, [x1, x2, x3, x4]) == sqrt(30.0)
+    g = ones(4)
+    MOI.eval_objective_gradient(evaluator, g, [x1, x2, x3, x4])
+    @test g == [
+        1.0 / sqrt(30.0),
+        2.0 / sqrt(30.0),
+        3.0 / sqrt(30.0),
+        4.0 / sqrt(30.0),
+    ]
+    return
+end
+
+function test_objective_norm_of_matrix_with_sum()
+    model = Nonlinear.Model()
+    x1 = MOI.VariableIndex(1)
+    x2 = MOI.VariableIndex(2)
+    x3 = MOI.VariableIndex(3)
+    x4 = MOI.VariableIndex(4)
+    Nonlinear.set_objective(model, :(norm([$x1 $x2; $x3 $x4] - [1 1; 1 1])))
+    evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x1, x2, x3, x4])
+    MOI.initialize(evaluator, [:Grad])
+    sizes = evaluator.backend.objective.expr.sizes
+    @test sizes.ndims == [0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0]
+    @test sizes.size_offset ==
+          [0, 12, 10, 8, 0, 0, 6, 0, 0, 4, 2, 0, 0, 0, 0, 0]
+    @test sizes.size == [1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2]
+    @test sizes.storage_offset ==
+          [0, 1, 5, 9, 11, 12, 13, 15, 16, 17, 21, 23, 24, 25, 27, 28, 29]
+    x1 = 1.0
+    x2 = 2.0
+    x3 = 3.0
+    x4 = 4.0
+    @test MOI.eval_objective(evaluator, [x1, x2, x3, x4]) == sqrt(14.0)
+    g = ones(4)
+    MOI.eval_objective_gradient(evaluator, g, [x1, x2, x3, x4])
+    @test g == [0.0, 1.0 / sqrt(14.0), 2.0 / sqrt(14.0), 3.0 / sqrt(14.0)]
+    return
+end
 
 end  # module