Rename all macros and definitions prefixed by 'PROXNLP_*'

CMakeLists.txt

@@ -85,12 +85,12 @@ endif(INITIALIZE_WITH_NAN)
 
 if(CHECK_RUNTIME_MALLOC)
   message(STATUS "Check if some memory allocations are performed at runtime.")
-  add_compile_definitions(PROXNLP_EIGEN_CHECK_MALLOC)
+  add_compile_definitions(PROXSUITE_NLP_EIGEN_CHECK_MALLOC)
   add_definitions(-DEIGEN_RUNTIME_NO_MALLOC)
 endif(CHECK_RUNTIME_MALLOC)
 
 if(ENABLE_TEMPLATE_INSTANTIATION)
-  add_definitions(-DPROXNLP_ENABLE_TEMPLATE_INSTANTIATION)
+  add_definitions(-DPROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION)
 endif(ENABLE_TEMPLATE_INSTANTIATION)
 
 macro(TAG_LIBRARY_VERSION target)
@@ -105,15 +105,15 @@ add_project_dependency(fmt "9.1.0...<11" REQUIRED PKG_CONFIG_REQUIRES "fmt >= 9.
 
 if(BUILD_WITH_PROXSUITE)
   add_project_dependency(proxsuite REQUIRED)
-  add_definitions(-DPROXNLP_ENABLE_PROXSUITE_LDLT)
+  add_definitions(-DPROXSUITE_NLP_USE_PROXSUITE_LDLT)
 endif()
 
 # Variable containing all the cflags definition relative to optional dependencies
 set(CFLAGS_DEPENDENCIES)
 
 if(BUILD_WITH_OPENMP_SUPPORT)
   find_package(OpenMP REQUIRED)
-  add_compile_definitions(PROXNLP_MULTITHREADING)
+  add_compile_definitions(PROXSUITE_NLP_MULTITHREADING)
 endif(BUILD_WITH_OPENMP_SUPPORT)
 
 set(BOOST_REQUIRED_COMPONENTS filesystem serialization system)
@@ -137,7 +137,7 @@ endif(BUILD_PYTHON_INTERFACE)
 if(BUILD_WITH_PINOCCHIO_SUPPORT)
   message(STATUS "Building with Pinocchio support.")
   add_project_dependency(pinocchio REQUIRED PKG_CONFIG_REQUIRES "pinocchio >= 2.9.1")
-  add_compile_definitions(PROXNLP_WITH_PINOCCHIO)
+  add_compile_definitions(PROXSUITE_NLP_WITH_PINOCCHIO)
 endif(BUILD_WITH_PINOCCHIO_SUPPORT)
 
 # --- MAIN LIBRARY ----------------------------------------
@@ -278,13 +278,13 @@ endmacro(
   var_value)
 
 if(BUILD_WITH_PINOCCHIO_SUPPORT)
-  export_variable(PROXNLP_WITH_PINOCCHIO_SUPPORT ON)
+  export_variable(PROXSUITE_NLP_WITH_PINOCCHIO_SUPPORT ON)
 endif()
 if(BUILD_WITH_CASADI_SUPPORT)
-  export_variable(PROXNLP_USE_CASADI ON)
+  export_variable(PROXSUITE_NLP_USE_CASADI ON)
 endif()
 if(BUILD_PYTHON_INTERFACE)
-  export_variable(PROXNLP_WITH_PYTHON_INTERFACE ON)
+  export_variable(PROXSUITE_NLP_WITH_PYTHON_INTERFACE ON)
 endif()
 
 pkg_config_append_libs(${PROJECT_NAME})

doc/Doxyfile.extra.in

@@ -20,7 +20,7 @@ ENABLE_PREPROCESSING    = YES
 MACRO_EXPANSION         = YES
 EXPAND_ONLY_PREDEF      = YES
 PREDEFINED              += EIGEN_MAKE_ALIGNED_OPERATOR_NEW \
-                           PROXNLP_DYNAMIC_TYPEDEFS
+                           PROXSUITE_NLP_DYNAMIC_TYPEDEFS
 
 FULL_PATH_NAMES         = YES
 EXCLUDE_SYMBOLS         = *::internal, internal::*, *::internal::*

examples/l1_qp.py

@@ -1,5 +1,5 @@
 """
-Solve an L1-penalized QP using PROXNLP.
+Solve an L1-penalized QP using proxsuite_nlp.
 """
 import proxsuite_nlp
 import numpy as np

examples/solo_inverse_geom.py

@@ -1,6 +1,6 @@
 """
 Inverse kinematics with friction cone constraint.
-We compare PROXNLP with the solver IPOPT.
+We compare proxsuite_nlp with the solver IPOPT.
 
 min Dq, f || q - q0 ||**2 + || f ||**2
 
@@ -213,7 +213,7 @@ def compute_cost(q_ref, fs):
     viz.display(qopt_ipopt)
 
 
-# Problem PROXNLP
+# Problem proxsuite_nlp
 
 xspace = MultibodyPhaseSpace(model)
 pb_space = VectorSpace(4 * 3 + (xspace.ndx))

examples/ur5-ik.cpp

@@ -15,7 +15,7 @@
 #include <boost/filesystem/path.hpp>
 
 using Scalar = double;
-PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
 using namespace proxnlp;
 namespace pin = pinocchio;

include/proxnlp/constraint-base.hpp

@@ -16,7 +16,7 @@ namespace proxnlp {
 template <typename _Scalar> struct ConstraintSetBase {
 public:
   using Scalar = _Scalar;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   using ActiveType = Eigen::Matrix<bool, Eigen::Dynamic, 1>;
   using Self = ConstraintSetBase<Scalar>;
 
@@ -117,7 +117,7 @@ template <typename _Scalar> struct ConstraintSetBase {
  */
 template <typename _Scalar> struct ConstraintObjectTpl {
   using Scalar = _Scalar;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
   using FunctionType = C2FunctionTpl<Scalar>;
   using ConstraintSet = ConstraintSetBase<Scalar>;

include/proxnlp/context.hpp

@@ -7,7 +7,7 @@ namespace context {
 
 using Scalar = double;
 
-PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 using VectorXBool = Eigen::Matrix<bool, Eigen::Dynamic, 1>;
 
 using BCLParams = ::proxnlp::BCLParamsTpl<Scalar>;

include/proxnlp/cost-function.hpp

@@ -14,7 +14,7 @@ template <typename Scalar>
 auto downcast_function_to_cost(const shared_ptr<C2FunctionTpl<Scalar>> &func)
     -> shared_ptr<CostFunctionBaseTpl<Scalar>> {
   if (func->nr() != 1) {
-    PROXNLP_RUNTIME_ERROR(
+    PROXSUITE_NLP_RUNTIME_ERROR(
         "Function cannot be cast to cost (codimension nr != 1).");
   }
   return std::make_shared<func_to_cost<Scalar>>(func);
@@ -29,7 +29,7 @@ template <typename _Scalar>
 struct CostFunctionBaseTpl : public C2FunctionTpl<_Scalar> {
 public:
   using Scalar = _Scalar;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   using Base = C2FunctionTpl<Scalar>;
 
   CostFunctionBaseTpl(const int nx, const int ndx) : Base(nx, ndx, 1) {}
@@ -90,7 +90,7 @@ struct CostFunctionBaseTpl : public C2FunctionTpl<_Scalar> {
 
 template <typename _Scalar> struct func_to_cost : CostFunctionBaseTpl<_Scalar> {
   using Scalar = _Scalar;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   using Base = CostFunctionBaseTpl<Scalar>;
   using C2Function = C2FunctionTpl<Scalar>;
 
@@ -118,6 +118,6 @@ template <typename _Scalar> struct func_to_cost : CostFunctionBaseTpl<_Scalar> {
 
 } // namespace proxnlp
 
-#ifdef PROXNLP_ENABLE_TEMPLATE_INSTANTIATION
+#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
 #include "proxnlp/cost-function.txx"
 #endif

include/proxnlp/cost-sum.hpp

@@ -11,7 +11,7 @@ namespace proxnlp {
 template <typename _Scalar> struct CostSumTpl : CostFunctionBaseTpl<_Scalar> {
 public:
   using Scalar = _Scalar;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   using Base = CostFunctionBaseTpl<Scalar>;
   using BasePtr = shared_ptr<Base>;
 
@@ -114,6 +114,6 @@ template <typename _Scalar> struct CostSumTpl : CostFunctionBaseTpl<_Scalar> {
 
 #include "proxnlp/cost-sum.hxx"
 
-#ifdef PROXNLP_ENABLE_TEMPLATE_INSTANTIATION
+#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
 #include "proxnlp/cost-sum.txx"
 #endif

include/proxnlp/exceptions.hpp

@@ -5,22 +5,23 @@
 #include <stdexcept>
 #include <fmt/core.h>
 
-#define PROXNLP_RUNTIME_ERROR(msg)                                             \
+#define PROXSUITE_NLP_RUNTIME_ERROR(msg)                                       \
   throw ::proxnlp::RuntimeError(                                               \
       fmt::format("{}({}): {}", __FILE__, __LINE__, msg))
 
-#define PROXNLP_DIM_CHECK(x, nx)                                               \
+#define PROXSUITE_NLP_DIM_CHECK(x, nx)                                         \
   if (x.size() != nx)                                                          \
-  PROXNLP_RUNTIME_ERROR(fmt::format(                                           \
+  PROXSUITE_NLP_RUNTIME_ERROR(fmt::format(                                     \
       "Input size invalid (expected {:d}, got {:d})", nx, x.size()))
 
-#define PROXNLP_RAISE_IF_NAN(value)                                            \
+#define PROXSUITE_NLP_RAISE_IF_NAN(value)                                      \
   if (::proxnlp::math::check_value(value))                                     \
-  PROXNLP_RUNTIME_ERROR("Encountered NaN.\n")
+  PROXSUITE_NLP_RUNTIME_ERROR("Encountered NaN.\n")
 
-#define PROXNLP_RAISE_IF_NAN_NAME(value, name)                                 \
+#define PROXSUITE_NLP_RAISE_IF_NAN_NAME(value, name)                           \
   if (::proxnlp::math::check_value(value))                                     \
-  PROXNLP_RUNTIME_ERROR(fmt::format("Encountered NaN for value {:s}.\n", name))
+  PROXSUITE_NLP_RUNTIME_ERROR(                                                 \
+      fmt::format("Encountered NaN for value {:s}.\n", name))
 
 namespace proxnlp {
 

include/proxnlp/function-base.hpp

@@ -17,7 +17,7 @@ template <typename _Scalar> struct BaseFunctionTpl : math_types<_Scalar> {
 
 public:
   using Scalar = _Scalar;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
   BaseFunctionTpl(const int nx, const int ndx, const int nr)
       : nx_(nx), ndx_(ndx), nr_(nr) {}
@@ -46,7 +46,7 @@ struct C1FunctionTpl : public BaseFunctionTpl<_Scalar> {
   using Scalar = _Scalar;
   using Base = BaseFunctionTpl<_Scalar>;
   using Base::Base;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
   /// @brief      Jacobian matrix of the constraint function.
   virtual void computeJacobian(const ConstVectorRef &x,
@@ -72,7 +72,7 @@ struct C2FunctionTpl : public C1FunctionTpl<_Scalar> {
   using Scalar = _Scalar;
   using Base = C1FunctionTpl<_Scalar>;
   using Base::Base;
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
   /// @brief      Vector-hessian product.
   virtual void vectorHessianProduct(const ConstVectorRef &,
@@ -84,6 +84,6 @@ struct C2FunctionTpl : public C1FunctionTpl<_Scalar> {
 
 } // namespace proxnlp
 
-#ifdef PROXNLP_ENABLE_TEMPLATE_INSTANTIATION
+#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
 #include "proxnlp/function-base.txx"
 #endif

include/proxnlp/function-ops.hpp

@@ -17,14 +17,14 @@ template <typename _Scalar> struct ComposeFunctionTpl : C2FunctionTpl<_Scalar> {
   using Base::computeJacobian;
   using Base::vectorHessianProduct;
 
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
   ComposeFunctionTpl(const shared_ptr<Base> &left,
                      const shared_ptr<Base> &right)
       : Base(right->nx(), right->ndx(), left->nr()), left_(left),
         right_(right) {
     if (left->nx() != right->nr()) {
-      PROXNLP_RUNTIME_ERROR(fmt::format(
+      PROXSUITE_NLP_RUNTIME_ERROR(fmt::format(
           "Incompatible dimensions ({:d} and {:d}).", left->nx(), right->nr()));
     }
     assert(left->nx() == right->nr());
@@ -59,6 +59,6 @@ auto compose(const shared_ptr<C2FunctionTpl<Scalar>> &left,
 
 } // namespace proxnlp
 
-#ifdef PROXNLP_ENABLE_TEMPLATE_INSTANTIATION
+#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
 #include "proxnlp/function-ops.txx"
 #endif

include/proxnlp/helpers/history-callback.hpp

@@ -17,7 +17,7 @@ template <typename Scalar> struct history_callback : base_callback<Scalar> {
       : store_primal_dual_vars_(store_pd_vars), store_values_(store_values),
         store_residuals_(store_residuals) {}
 
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
 
   struct {
     std::vector<VectorXs> xs;

include/proxnlp/ldlt-allocator.hpp

@@ -6,7 +6,7 @@
 
 #include "proxnlp/linalg/block-ldlt.hpp"
 #include "proxnlp/linalg/bunchkaufman.hpp"
-#ifdef PROXNLP_ENABLE_PROXSUITE_LDLT
+#ifdef PROXSUITE_NLP_USE_PROXSUITE_LDLT
 #include "proxnlp/linalg/proxsuite-ldlt-wrap.hpp"
 #endif
 #include <boost/variant.hpp>
@@ -36,7 +36,7 @@ template <typename Scalar,
 using LDLTVariant =
     boost::variant<linalg::DenseLDLT<Scalar>, linalg::BlockLDLT<Scalar>,
                    Eigen::LDLT<MatrixType>, Eigen::BunchKaufman<MatrixType>
-#ifdef PROXNLP_ENABLE_PROXSUITE_LDLT
+#ifdef PROXSUITE_NLP_USE_PROXSUITE_LDLT
                    ,
                    linalg::ProxSuiteLDLTWrapper<Scalar>
 #endif
@@ -119,10 +119,10 @@ LDLTVariant<Scalar> allocate_ldlt_from_sizes(const std::vector<isize> &nprims,
   case LDLTChoice::EIGEN:
     return Eigen::LDLT<MatrixXs>(size);
   case LDLTChoice::PROXSUITE:
-#ifdef PROXNLP_ENABLE_PROXSUITE_LDLT
+#ifdef PROXSUITE_NLP_USE_PROXSUITE_LDLT
     return linalg::ProxSuiteLDLTWrapper<Scalar>(size, size);
 #else
-    PROXNLP_RUNTIME_ERROR(
+    PROXSUITE_NLP_RUNTIME_ERROR(
         "ProxSuite support is not enabled. You should recompile ProxNLP with "
         "the BUILD_WITH_PROXSUITE flag.");
 #endif
@@ -210,7 +210,7 @@ computeInertiaTuple(const Eigen::Ref<Eigen::VectorXi const> &signature) {
       nn++;
       break;
     default:
-      PROXNLP_RUNTIME_ERROR(
+      PROXSUITE_NLP_RUNTIME_ERROR(
           "Signature vector should only contain values O, 1, -1.");
       break;
     }

include/proxnlp/linalg/block-ldlt.hpp

@@ -19,7 +19,7 @@ namespace backend {
 
 /// Implementation struct for the recursive block LDLT algorithm.
 template <typename Scalar> struct block_impl {
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   MatrixRef mat;
   SymbolicBlockMatrix sym_structure;
   /// @returns bool whether the decomposition was successful.
@@ -194,7 +194,7 @@ template <typename Scalar> struct block_impl {
 /// of structure should lead to recalculating the expected sparsity pattern of
 /// the factorization, and even recomputing the sparsity-optimal permutation.
 template <typename Scalar> struct BlockLDLT : ldlt_base<Scalar> {
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   using Base = ldlt_base<Scalar>;
   using DView = typename Base::DView;
   using Traits = LDLT_Traits<MatrixXs, Eigen::Lower>;
@@ -340,6 +340,6 @@ template <typename Scalar> struct BlockLDLT : ldlt_base<Scalar> {
 
 #include "./block-ldlt.hxx"
 
-#ifdef PROXNLP_ENABLE_TEMPLATE_INSTANTIATION
+#ifdef PROXSUITE_NLP_ENABLE_TEMPLATE_INSTANTIATION
 #include "./block-ldlt.txx"
 #endif

include/proxnlp/linalg/block-ldlt.hxx

@@ -73,7 +73,7 @@ template <typename Derived>
 bool BlockLDLT<Scalar>::solveInPlace(Eigen::MatrixBase<Derived> &b) const {
 
   b.noalias() = permutationP().transpose() * b;
-  PROXNLP_NOMALLOC_BEGIN;
+  PROXSUITE_NLP_NOMALLOC_BEGIN;
   BlockTriL mat_blk_L(m_matrix, m_structure);
   bool flag = mat_blk_L.solveInPlace(b);
 
@@ -91,7 +91,7 @@ bool BlockLDLT<Scalar>::solveInPlace(Eigen::MatrixBase<Derived> &b) const {
   BlockTriU mat_blk_U(m_matrix.transpose(), m_struct_tr);
   flag |= mat_blk_U.solveInPlace(b);
 
-  PROXNLP_NOMALLOC_END;
+  PROXSUITE_NLP_NOMALLOC_END;
   b.noalias() = permutationP() * b;
   return flag;
 }

include/proxnlp/linalg/dense.hpp

@@ -161,7 +161,7 @@ dense_ldlt_reconstruct(typename math_types<Scalar>::ConstMatrixRef const &mat,
 
 /// @brief  A fast, recursive divide-and-conquer LDLT algorithm.
 template <typename Scalar> struct DenseLDLT : ldlt_base<Scalar> {
-  PROXNLP_DYNAMIC_TYPEDEFS(Scalar);
+  PROXSUITE_NLP_DYNAMIC_TYPEDEFS(Scalar);
   using Base = ldlt_base<Scalar>;
   using DView = typename Base::DView;
   using MatrixType = MatrixXs;