add dimension check

src/objective.jl

@@ -532,6 +532,13 @@ function set_objective_coefficients(
     if _nlp_objective_function(model) !== nothing
         error("A nonlinear objective is already set in the model")
     end
+    if length(variables) != length(coeffs)
+        throw(
+            DimensionMismatch(
+                "The number of variables and coefficients must match",
+            ),
+        )
+    end
     coeffs_t = convert.(T, coeffs)::AbstractVector{T}
     F = objective_function_type(model)
     _set_objective_coefficients(model, variables, coeffs_t, F)
@@ -702,6 +709,13 @@ function set_objective_coefficients(
     if _nlp_objective_function(model) !== nothing
         error("A nonlinear objective is already set in the model")
     end
+    if !(length(variables_1) == length(variables_2) == length(coeffs))
+        throw(
+            DimensionMismatch(
+                "The number of variables and coefficients must match",
+            ),
+        )
+    end
     coeffs_t = convert.(T, coeffs)::AbstractVector{<:T}
     F = moi_function_type(objective_function_type(model))
     _set_objective_coefficients(model, variables_1, variables_2, coeffs_t, F)

test/test_objective.jl

@@ -72,6 +72,19 @@ function test_objective_coef_update_linear_objective_batch_error()
     return
 end
 
+function test_objective_coef_update_linear_objective_batch_dimension_error()
+    model = Model()
+    @variable(model, x[1:2])
+    @objective(model, Min, x[1] * x[2])
+    @test_throws(
+        DimensionMismatch(
+            "The number of variables and coefficients must match",
+        ),
+        set_objective_coefficients(model, [x[1], x[2]], [2]),
+    )
+    return
+end
+
 function test_objective_coef_batch_update_linear_objective_changes()
     model = Model()
     @variable(model, x)
@@ -325,6 +338,19 @@ function test_set_objective_coefficient_quadratic_batch_error()
     return
 end
 
+function test_set_objective_coefficient_quadratic_batch_dimension_error()
+    model = Model()
+    @variable(model, x[1:2])
+    @objective(model, Min, x[1] * x[2])
+    @test_throws(
+        DimensionMismatch(
+            "The number of variables and coefficients must match",
+        ),
+        set_objective_coefficients(model, [x[1]], [x[1]], [2, 3]),
+    )
+    return
+end
+
 function test_set_objective_coefficient_quadratic_affine_original()
     model = Model()
     @variable(model, x[1:2])