Rewrite the C wrapper

.codecov.yml

@@ -0,0 +1,2 @@
+ignore:
+ - 'src/libknitro.jl'

examples/advanced/space_shuttle/space_shuttle.jl

@@ -301,12 +301,12 @@ objIndex, objCoef = ind_θ[end], 1.0
 KNITRO.KN_add_obj_linear_struct(kc, objIndex - 1, objCoef)
 KNITRO.KN_set_obj_goal(kc, KNITRO.KN_OBJGOAL_MAXIMIZE)
 
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_ALG, KNITRO.KN_ALG_BAR_DIRECT)
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_BAR_MURULE, KNITRO.KN_BAR_MURULE_QUALITY)
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_LINSOLVER, KNITRO.KN_LINSOLVER_MA27)
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_ALG, KNITRO.KN_ALG_BAR_DIRECT)
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_BAR_MURULE, KNITRO.KN_BAR_MURULE_QUALITY)
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_LINSOLVER, KNITRO.KN_LINSOLVER_MA27)
 
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_HESSOPT, KNITRO.KN_HESSOPT_LBFGS)
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_LMSIZE, 5)  # limited-memory pairs stored
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_HESSOPT, KNITRO.KN_HESSOPT_LBFGS)
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_LMSIZE, 5)  # limited-memory pairs stored
 
 KNITRO.KN_solve(kc)
 nStatus, objSol, x, lambda_ = KNITRO.KN_get_solution(kc)

examples/conic.jl

@@ -34,7 +34,7 @@ function example_conic(; verbose=true)
     ####*  unbounded below and any unset upper bounds are
     ####*  assumed to be unbounded above. */
     n = 4
-    KNITRO.KN_add_vars(kc, n)
+    KNITRO.KN_add_vars(kc, n, C_NULL)
 
     xLoBnds = [-KNITRO.KN_INFINITY, 1.0, -KNITRO.KN_INFINITY, 2.0]
     xUpBnds = [KNITRO.KN_INFINITY, KNITRO.KN_INFINITY, 1.0, KNITRO.KN_INFINITY]
@@ -43,31 +43,31 @@ function example_conic(; verbose=true)
 
     #** Add the constraints and set the RHS and coefficients */
     m = 3
-    KNITRO.KN_add_cons(kc, m)
+    KNITRO.KN_add_cons(kc, m, C_NULL)
     KNITRO.KN_set_con_upbnd(kc, 0, 0.0)
     KNITRO.KN_set_con_upbnd(kc, 1, 100.0)
     KNITRO.KN_set_con_upbnd(kc, 2, 100.0)
 
     #** coefficients for linear terms in constraint c2 */
     indexVars1 = Cint[1, 2]
     coefs1 = [2.0, 3.0]
-    KNITRO.KN_add_con_linear_struct(kc, 2, indexVars1, coefs1)
+    KNITRO.KN_add_con_linear_struct_one(kc, 2, 2, indexVars1, coefs1)
 
     #** coefficient for linear term in constraint c1 */
     indexVars2 = Cint[0]
     coefs2 = [5.0]
-    KNITRO.KN_add_con_linear_struct(kc, 1, indexVars2, coefs2)
+    KNITRO.KN_add_con_linear_struct_one(kc, 1, 1, indexVars2, coefs2)
 
     #** coefficient for linear term in constraint c0 */
     indexVars3 = Cint[1]
     coefs3 = [-10.0]
-    KNITRO.KN_add_con_linear_struct(kc, 0, indexVars3, coefs3)
+    KNITRO.KN_add_con_linear_struct_one(kc, 1, 0, indexVars3, coefs3)
 
     #** coefficient for quadratic term in constraint c1 */
-    qconIndexVar1 = 3
-    qconIndexVar2 = 3
-    qconCoef = 1.0
-    KNITRO.KN_add_con_quadratic_struct(kc, 1, qconIndexVar1, qconIndexVar2, qconCoef)
+    qconIndexVar1 = Cint[3]
+    qconIndexVar2 = Cint[3]
+    qconCoef = [1.0]
+    KNITRO.KN_add_con_quadratic_struct_one(kc, 1, 1, qconIndexVar1, qconIndexVar2, qconCoef)
 
     #** Coefficients for L2-norm constraint components in c0.
     #*  Assume the form ||Ax+b|| (here with b = 0)
@@ -90,48 +90,51 @@ function example_conic(; verbose=true)
     qobjIndexVars1 = Cint[0, 2, 3, 2, 1]
     qobjIndexVars2 = Cint[0, 2, 3, 3, 1]
     qobjCoefs = [1.0, 1.0, 1.0, 2.0, 1.0]
-    KNITRO.KN_add_obj_quadratic_struct(kc, qobjIndexVars1, qobjIndexVars2, qobjCoefs)
+    KNITRO.KN_add_obj_quadratic_struct(kc, 5, qobjIndexVars1, qobjIndexVars2, qobjCoefs)
 
     #** Add linear objective term. */
     lobjIndexVar = Cint[2]
     lobjCoef = [1.0]
-    KNITRO.KN_add_obj_linear_struct(kc, lobjIndexVar, lobjCoef)
+    KNITRO.KN_add_obj_linear_struct(kc, 1, lobjIndexVar, lobjCoef)
 
     #** Interior/Direct algorithm is required for models with
     #*  L2 norm structure.
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_ALGORITHM, KNITRO.KN_ALG_BAR_DIRECT)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_ALGORITHM, KNITRO.KN_ALG_BAR_DIRECT)
     #** Enable the special barrier tools for second order cone(SOC) constraints. */
-    KNITRO.KN_set_param(
+    KNITRO.KN_set_int_param(
         kc,
         KNITRO.KN_PARAM_BAR_CONIC_ENABLE,
         KNITRO.KN_BAR_CONIC_ENABLE_SOC,
     )
     #** Specify maximum output */
     outlev = verbose ? KNITRO.KN_OUTLEV_ALL : KNITRO.KN_OUTLEV_NONE
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_OUTLEV, outlev)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_OUTLEV, outlev)
     #** Specify special barrier update rule */
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_BAR_MURULE, KNITRO.KN_BAR_MURULE_FULLMPC)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_BAR_MURULE, KNITRO.KN_BAR_MURULE_FULLMPC)
 
     #** Solve the problem.
     ####*
     ####*  Return status codes are defined in "knitro.h" and described
     ####*  in the Knitro manual. */
     nStatus = KNITRO.KN_solve(kc)
     nStatus, objSol, x, _ = KNITRO.KN_get_solution(kc)
-    feasError = KNITRO.KN_get_abs_feas_error(kc)
-    optError = KNITRO.KN_get_abs_opt_error(kc)
+    feasError = Ref{Cdouble}()
+    KNITRO.KN_get_abs_feas_error(kc, feasError)
+    optError = Ref{Cdouble}()
+    KNITRO.KN_get_abs_opt_error(kc, optError)
 
     #** An example of obtaining solution information. */
     if verbose
         println("Knitro converged with final status = ", nStatus)
         println("  optimal objective value  = ", objSol)
         println("  optimal primal values x  = ", x)
-        println("  feasibility violation    = ", feasError)
-        println("  KKT optimality violation = ", optError)
+        println("  feasibility violation    = ", feasError[])
+        println("  KKT optimality violation = ", optError[])
     end
 
     #** Delete the Knitro solver instance. */
-    return KNITRO.KN_free(kc)
+    KNITRO.KN_free(kc)
+    return
 end
 
 example_conic(; verbose=isdefined(Main, :KN_VERBOSE) ? KN_VERBOSE : true)

examples/fcga.jl

@@ -108,20 +108,21 @@ function example_fcga(; verbose=true)
     # Note: any unset lower bounds are assumed to be
     # unbounded below and any unset upper bounds are
     # assumed to be unbounded above.
-    vars = KNITRO.KN_add_vars(kc, 4)
+    vars = zeros(Cint, 4)
+    KNITRO.KN_add_vars(kc, 4, vars)
     for x in vars
         KNITRO.KN_set_var_primal_init_value(kc, x, 0.8)
     end
 
     # Add the constraints and set the rhs and coefficients
-    KNITRO.KN_add_cons(kc, 3)
+    KNITRO.KN_add_cons(kc, 3, C_NULL)
     KNITRO.KN_set_con_eqbnds_all(kc, [1.0, 0.0, 0.0])
 
     # Coefficients for 2 linear terms
     lconIndexCons = Int32[1, 2]
     lconIndexVars = Int32[2, 1]
     lconCoefs = [-1.0, -1.0]
-    KNITRO.KN_add_con_linear_struct(kc, lconIndexCons, lconIndexVars, lconCoefs)
+    KNITRO.KN_add_con_linear_struct(kc, 2, lconIndexCons, lconIndexVars, lconCoefs)
 
     # Coefficients for 2 quadratic terms
 
@@ -132,6 +133,7 @@ function example_fcga(; verbose=true)
 
     KNITRO.KN_add_con_quadratic_struct(
         kc,
+        2,
         qconIndexCons,
         qconIndexVars1,
         qconIndexVars2,
@@ -185,26 +187,29 @@ function example_fcga(; verbose=true)
 
     # Set option to print output after every iteration.
     kn_outlev = verbose ? KNITRO.KN_OUTLEV_ITER : KNITRO.KN_OUTLEV_NONE
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
 
     # Set option to tell Knitro that the gradients are being provided
     # with the functions in one callback.
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_EVAL_FCGA, KNITRO.KN_EVAL_FCGA_YES)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_EVAL_FCGA, KNITRO.KN_EVAL_FCGA_YES)
 
     # Solve the problem.
     #
     # Return status codes are defined in "knitro.h" and described
     # in the Knitro manual.
     nStatus = KNITRO.KN_solve(kc)
     nStatus, objSol, x, lambda_ = KNITRO.KN_get_solution(kc)
-
     # An example of obtaining solution information.
     if verbose
+        feasError = Ref{Cdouble}()
+        KNITRO.KN_get_abs_feas_error(kc, feasError)
+        optError = Ref{Cdouble}()
+        KNITRO.KN_get_abs_opt_error(kc, optError)
         println("Knitro converged with final status = ", nStatus)
         println("  optimal objective value  = ", objSol)
         println("  optimal primal values x  = ", x)
-        println("  feasibility violation    = ", KNITRO.KN_get_abs_feas_error(kc))
-        println("  KKT optimality violation = ", KNITRO.KN_get_abs_opt_error(kc))
+        println("  feasibility violation    = ", feasError[])
+        println("  KKT optimality violation = ", optError[])
     end
 
     # Delete the Knitro solver instance.
@@ -215,6 +220,7 @@ function example_fcga(; verbose=true)
         @test objSol ≈ 0.25
         @test x ≈ [0.793701, 0.707107, 0.529732, 0.840896] atol = 1e-5
     end
+    return
 end
 
 example_fcga(; verbose=isdefined(Main, :KN_VERBOSE) ? KN_VERBOSE : true)

examples/licensemanager/nlp1.jl

@@ -113,7 +113,7 @@ KNITRO.KN_set_con_lobnds(kc, [1.0, 0.0])
 # structure for these constraints.
 
 # First load quadratic structure x0*x1 for the first constraint
-KNITRO.KN_add_con_quadratic_struct(kc, 0, 0, 1, 1.0)
+KNITRO.KN_add_con_quadratic_struct_one(kc, 1, 0, Cint[0], Cint[1], [1.0])
 
 # Load structure for the second constraint.  below we add the linear
 # structure and the quadratic structure separately, though it
@@ -122,10 +122,10 @@ KNITRO.KN_add_con_quadratic_struct(kc, 0, 0, 1, 1.0)
 # supports adding linear terms.
 
 # Add linear term x0 in the second constraint
-KNITRO.KN_add_con_linear_struct(kc, 1, 0, 1.0)
+KNITRO.KN_add_con_linear_struct_one(kc, 1, 1, Cint[0], [1.0])
 
 # Add quadratic term x1^2 in the second constraint
-KNITRO.KN_add_con_quadratic_struct(kc, 1, 1, 1, 1.0)
+KNITRO.KN_add_con_quadratic_struct_one(kc, 1, 1, Cint[1], Cint[1], [1.0])
 
 # Add a callback function "callbackEvalF" to evaluate the nonlinear
 #(non-quadratic) objective.  Note that the linear and
@@ -160,13 +160,13 @@ KNITRO.KN_set_cb_hess(kc, cb, KNITRO.KN_DENSE_ROWMAJOR, callbackEvalH!)
 # Specify that the user is able to provide evaluations
 # of the hessian matrix without the objective component.
 # turned off by default but should be enabled if possible.
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_HESSIAN_NO_F, KNITRO.KN_HESSIAN_NO_F_ALLOW)
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_HESSIAN_NO_F, KNITRO.KN_HESSIAN_NO_F_ALLOW)
 
 # Set minimize or maximize(if not set, assumed minimize)
 KNITRO.KN_set_obj_goal(kc, KNITRO.KN_OBJGOAL_MINIMIZE)
 
 # Perform a derivative check.
-KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_DERIVCHECK, KNITRO.KN_DERIVCHECK_ALL)
+KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_DERIVCHECK, KNITRO.KN_DERIVCHECK_ALL)
 
 # Solve the problem.
 #

examples/lp1.jl

@@ -36,13 +36,15 @@ function example_lp1(; verbose=true)
 
     # Add the variables and set their bounds.
     # Note: unset bounds assumed to be infinite.
-    xIndices = KNITRO.KN_add_vars(kc, 4)
+    xIndices = zeros(Cint, 4)
+    KNITRO.KN_add_vars(kc, 4, xIndices)
     for x in xIndices
         KNITRO.KN_set_var_lobnd(kc, x, 0.0)
     end
 
     # Add the constraints and set the rhs and coefficients.
-    cons = KNITRO.KN_add_cons(kc, 2)
+    cons = zeros(Cint, 2)
+    KNITRO.KN_add_cons(kc, 2, cons)
     KNITRO.KN_set_con_eqbnds_all(kc, [5.0, 8.0])
     # Add Jacobian structure and coefficients.
     # First constraint
@@ -53,8 +55,8 @@ function example_lp1(; verbose=true)
     jacIndexCons = [jacIndexCons; Int32[1, 1, 1]]
     jacIndexVars = [jacIndexVars; Int32[0, 1, 3]]
     jacCoefs = [jacCoefs; [2.0, 0.5, 1.0]]
-    KNITRO.KN_add_con_linear_struct(kc, 0, Int32[0, 1, 2], [1.0, 1.0, 1.0])
-    KNITRO.KN_add_con_linear_struct(kc, 1, Int32[0, 1, 3], [2.0, 0.5, 1.0])
+    KNITRO.KN_add_con_linear_struct_one(kc, 3, 0, Int32[0, 1, 2], [1.0, 1.0, 1.0])
+    KNITRO.KN_add_con_linear_struct_one(kc, 3, 1, Int32[0, 1, 3], [2.0, 0.5, 1.0])
 
     # Set minimize or maximize (if not set, assumed minimize).
     KNITRO.KN_set_obj_goal(kc, KNITRO.KN_OBJGOAL_MINIMIZE)
@@ -65,7 +67,7 @@ function example_lp1(; verbose=true)
     KNITRO.KN_add_obj_linear_struct(kc, 2, objIndices, objCoefs)
 
     kn_outlev = verbose ? KNITRO.KN_OUTLEV_ALL : KNITRO.KN_OUTLEV_NONE
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
 
     # Solve the problem.
     #
@@ -75,11 +77,15 @@ function example_lp1(; verbose=true)
     nStatus, objSol, x, lambda_ = KNITRO.KN_get_solution(kc)
 
     if verbose
+        feasError = Ref{Cdouble}()
+        KNITRO.KN_get_abs_feas_error(kc, feasError)
+        optError = Ref{Cdouble}()
+        KNITRO.KN_get_abs_opt_error(kc, optError)
         println("Knitro converged with final status = ", nStatus)
         println("  optimal objective value  = ", objSol)
         println("  optimal primal values x  = ", x)
-        println("  feasibility violation    = ", KNITRO.KN_get_abs_feas_error(kc))
-        println("  KKT optimality violation = ", KNITRO.KN_get_abs_opt_error(kc))
+        println("  feasibility violation    = ", feasError[])
+        println("  KKT optimality violation = ", optError[])
     end
 
     # Delete the Knitro solver instance.

examples/lsq1.jl

@@ -42,11 +42,11 @@ function example_lsq1(; verbose=true)
     # unbounded below and any unset upper bounds are
     # assumed to be unbounded above.
     n = 3 # # of variables/parameters
-    KNITRO.KN_add_vars(kc, n)
+    KNITRO.KN_add_vars(kc, n, C_NULL)
 
     # Add the residuals.
     m = 5 # # of residuals
-    KNITRO.KN_add_rsds(kc, m)
+    KNITRO.KN_add_rsds(kc, m, C_NULL)
 
     # Set the array of constants, y, in the residuals
     KNITRO.KN_add_rsd_constants_all(kc, [1.0, 0.5, 0.0, 0.5, 2.0])
@@ -79,10 +79,10 @@ function example_lsq1(; verbose=true)
     coefs = [coefs; [-1.0, -1.0, -1.0]]
 
     # Pass in the linear coefficients
-    KNITRO.KN_add_rsd_linear_struct(kc, indexRsds, indexVars, coefs)
+    KNITRO.KN_add_rsd_linear_struct(kc, length(indexRsds), indexRsds, indexVars, coefs)
 
     kn_outlev = verbose ? KNITRO.KN_OUTLEV_ALL : KNITRO.KN_OUTLEV_NONE
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
 
     # Solve the problem.
     #

examples/lsq2.jl

@@ -98,7 +98,7 @@ function example_lsq2(; verbose=true)
     # unbounded below and any unset upper bounds are
     # assumed to be unbounded above.
     n = 2 # # of variables/parameters
-    KNITRO.KN_add_vars(kc, n)
+    KNITRO.KN_add_vars(kc, n, C_NULL)
 
     # In order to prevent the possiblity of numerical
     # overflow from very large numbers, we set a
@@ -110,7 +110,7 @@ function example_lsq2(; verbose=true)
 
     # Add the residuals.
     m = 6 # # of residuals
-    KNITRO.KN_add_rsds(kc, m)
+    KNITRO.KN_add_rsds(kc, m, C_NULL)
 
     # Set the array of constants in the residuals
     KNITRO.KN_add_rsd_constants_all(kc, [-2.138, -3.421, -3.597, -4.34, -4.882, -5.66])
@@ -132,7 +132,7 @@ function example_lsq2(; verbose=true)
     KNITRO.KN_set_cb_rsd_jac(kc, cb, KNITRO.KN_DENSE_ROWMAJOR, callbackEvalRJ)
 
     kn_outlev = verbose ? KNITRO.KN_OUTLEV_ALL : KNITRO.KN_OUTLEV_NONE
-    KNITRO.KN_set_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
+    KNITRO.KN_set_int_param(kc, KNITRO.KN_PARAM_OUTLEV, kn_outlev)
 
     # Solve the problem.
     #