Merge remote-tracking branch 'origin/develop-0.6' into develop-1.0

laoyuer · Oct 14, 2019 · b72a2fe · b72a2fe
2 parents fab90f8 + ffd5879
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diff --git a/.bumpversion.cfg b/.bumpversion.cfg
@@ -1,6 +1,7 @@
 [bumpversion]
 current_version = 0.6.0
 commit = True
+tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\.(?P<release>[a-z]+)(?P<dev>\d+))?
 serialize = 
 	{major}.{minor}.{patch}.{release}{dev}

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,10 +1,11 @@
-Version 0.6.0
+Version 0.6.0 (30 August 2019)
 ------------------------------
 * Added meaningful return values to internal functions. Changed syntax of `osqp_setup` function. It now returns an exitflag.
 * `osqp_setup` function requires `P` to be upper triangular. It returns a nonzero exitflag otherwise.
 * Custom memory allocators via cmake and the configure file.
 * Changed interfaces to linsys solver functions. The solve function now computes `(x_tilde,z_tilde)` instead of `(x_tilde,nu)`. This allows to implement custom linear system solvers (also indirect).
 * Added `solve` function in Python interface that performs `setup` `solve` and `cleanup` for you directly and disables GIL.
+* Improved code generation folder structure.
 
 
 Version 0.5.0 (10 December 2018)

diff --git a/README.md b/README.md
@@ -20,7 +20,7 @@ minimize        0.5 x' P x + q' x
 subject to      l <= A x <= u
 ```
 
-where `x in R^n` is the optimization variable. The objective function is defined by a positive semidefinite matrix `P in S^n_+` and vector `q in R^n`. The linear constraints are defined by matrix `A in R^{m x n}` and vectors `l in R^m U {-inf}^m`, `u in R^m U {+inf}^m`.
+where `x in R^n` is the optimization variable. The objective function is defined by a positive semidefinite matrix `P in S^n_+` and vector `q in R^n`. The linear constraints are defined by matrix `A in R^{m x n}` and vectors `l` and `u` so that `l_i in R U {-inf}` and `u_i in R U {+inf}` for all `i in 1,...,m`.
 
 
 The latest version is `0.6.0`.

diff --git a/ci/travis/script.sh b/ci/travis/script.sh
@@ -28,6 +28,18 @@ cd build
 cmake -G "Unix Makefiles" -DCOVERAGE=ON -DUNITTESTS=ON ..
 make
 ${TRAVIS_BUILD_DIR}/build/out/osqp_tester
+# Pefrorm code coverage (only in Linux case)
+if [[ $TRAVIS_OS_NAME == "linux" ]]; then
+    cd ${TRAVIS_BUILD_DIR}/build
+    lcov --directory . --capture -o coverage.info # capture coverage info
+    lcov --remove coverage.info "${TRAVIS_BUILD_DIR}/tests/*" \
+        "${TRAVIS_BUILD_DIR}/lin_sys/direct/qdldl/amd/*" \
+        "${TRAVIS_BUILD_DIR}/lin_sys/direct/qdldl/qdldl_sources/*" \
+        "/usr/include/x86_64-linux-gnu/**/*" \
+        -o coverage.info # filter out tests and unnecessary files
+    lcov --list coverage.info # debug before upload
+    coveralls-lcov coverage.info # uploads to coveralls
+fi
 
 if [[ $TRAVIS_OS_NAME == "linux" ]]; then
     echo "Testing OSQP with valgrind (disabling MKL pardiso for memory allocation issues)"
@@ -76,6 +88,22 @@ cd build
 cmake -G "Unix Makefiles" -DEMBEDDED=2 ..
 make
 
+echo "Building OSQP without profiling"
+cd ${TRAVIS_BUILD_DIR}
+rm -rf build
+mkdir build
+cd build
+cmake -G "Unix Makefiles" -DPROFILING=OFF ..
+make
+
+echo "Building OSQP without user interrupt"
+cd ${TRAVIS_BUILD_DIR}
+rm -rf build
+mkdir build
+cd build
+cmake -G "Unix Makefiles" -DCTRLC=OFF ..
+make
+
 echo "Testing OSQP without printing"
 cd ${TRAVIS_BUILD_DIR}
 rm -rf build

diff --git a/docs/_static/css/osqp_theme.css b/docs/_static/css/osqp_theme.css
@@ -192,6 +192,7 @@ p.breathe-sectiondef-title.rubric {
 
 
 /* Margin below tables */
-p {
+table p {
     margin-bottom: 0px;
 }
+
diff --git a/docs/citing/index.rst b/docs/citing/index.rst
@@ -33,11 +33,15 @@ If you use OSQP for published work, we encourage you to put a star on `GitHub <h
         .. code:: latex
 
           @article{osqp-infeasibility,
-            title   = {Infeasibility detection in the alternating direction method of multipliers for convex optimization},
-            journal = {Journal of Optimization Theory and Applications (To appear)},
             author  = {Banjac, G. and Goulart, P. and Stellato, B. and Boyd, S.},
+            title   = {Infeasibility detection in the alternating direction method of multipliers for convex optimization},
+            journal = {Journal of Optimization Theory and Applications},
             year    = {2019},
-            url     = {https://stanford.edu/~boyd/papers/pdf/admm_infeas.pdf},
+            volume  = {183},
+            number  = {2},
+            pages   = {490--519},
+            doi     = {10.1007/s10957-019-01575-y},
+            url     = {https://doi.org/10.1007/s10957-019-01575-y},
           }
 
     Code generation
@@ -46,11 +50,10 @@ If you use OSQP for published work, we encourage you to put a star on `GitHub <h
         .. code:: latex
 
           @inproceedings{osqp-codegen,
-            author = {Banjac, G. and Stellato, B. and Moehle, N. and Goulart, P. and Bemporad, A. and Boyd, S.},
-            title = {Embedded code generation using the {OSQP} solver},
-            booktitle = {{IEEE} Conference on Decision and Control ({CDC})},
-            year = {2017},
-            month = dec,
-            doi = {10.1109/CDC.2017.8263928},
-            url = {https://doi.org/10.1109/CDC.2017.8263928},
+            author    = {Banjac, G. and Stellato, B. and Moehle, N. and Goulart, P. and Bemporad, A. and Boyd, S.},
+            title     = {Embedded code generation using the {OSQP} solver},
+            booktitle = {IEEE Conference on Decision and Control (CDC)},
+            year      = {2017},
+            doi       = {10.1109/CDC.2017.8263928},
+            url       = {https://doi.org/10.1109/CDC.2017.8263928},
           }
diff --git a/docs/examples/setup-and-solve.rst b/docs/examples/setup-and-solve.rst
@@ -93,6 +93,32 @@ Julia
 
 
 
+R
+-
+
+.. code:: r
+
+    library(osqp)
+    library(Matrix)
+
+    # Define problem data
+    P <- Matrix(c(4., 1.,
+                  1., 2.), 2, 2, sparse = TRUE)
+    q <- c(1., 1.)
+    A <- Matrix(c(1., 1., 0.,
+                  1., 0., 1.), 3, 2, sparse = TRUE)
+    l <- c(1., 0., 0.)
+    u <- c(1., 0.7, 0.7)
+
+    # Change alpha parameter and setup workspace
+    settings <- osqpSettings(alpha = 1.0)
+    model <- osqp(P, q, A, l, u, settings)
+
+    # Solve problem
+    res <- model$Solve()
+
+
+
 C
 -
 

diff --git a/docs/examples/update-matrices.rst b/docs/examples/update-matrices.rst
@@ -128,6 +128,42 @@ Julia
 
 
 
+R
+-
+
+.. code:: r
+
+    library(osqp)
+    library(Matrix)
+
+    # Define problem data
+    P <- Matrix(c(4., 1.,
+                  1., 2.), 2, 2, sparse = TRUE)
+    q <- c(1., 1.)
+    A <- Matrix(c(1., 1., 0.,
+                  1., 0., 1.), 3, 2, sparse = TRUE)
+    l <- c(1., 0., 0.)
+    u <- c(1., 0.7, 0.7)
+
+    # Setup workspace
+    model <- osqp(P, q, A, l, u)
+
+    # Solve problem
+    res <- model$Solve()
+
+    # Update problem
+    # NB: Update only upper triangular part of P
+    P_new <- Matrix(c(5., 1.5,
+                      1.5, 1.), 2, 2, sparse = TRUE)
+    A_new <- Matrix(c(1.2, 1.5, 0.,
+                      1.1, 0., 0.8), 3, 2, sparse = TRUE)
+    model$Update(Px = P_new@x, Ax = A_new@x)
+
+    # Solve updated problem
+    res <- model$Solve()
+
+
+
 C
 -
 

diff --git a/docs/examples/update-vectors.rst b/docs/examples/update-vectors.rst
@@ -128,6 +128,40 @@ Julia
 
 
 
+R
+-
+
+.. code:: r
+
+    library(osqp)
+    library(Matrix)
+
+    # Define problem data
+    P <- Matrix(c(4., 1.,
+                  1., 2.), 2, 2, sparse = TRUE)
+    q <- c(1., 1.)
+    A <- Matrix(c(1., 1., 0.,
+                  1., 0., 1.), 3, 2, sparse = TRUE)
+    l <- c(1., 0., 0.)
+    u <- c(1., 0.7, 0.7)
+
+    # Setup workspace
+    model <- osqp(P, q, A, l, u)
+
+    # Solve problem
+    res <- model$Solve()
+
+    # Update problem
+    q_new <- c(2., 3.)
+    l_new <- c(2., -1., -1.)
+    u_new <- c(2., 2.5, 2.5)
+    model$Update(q = q_new, l = l_new, u = u_new)
+
+    # Solve updated problem
+    res <- model$Solve()
+
+
+
 C
 -
 

diff --git a/docs/get_started/linear_system_solvers.rst b/docs/get_started/linear_system_solvers.rst
@@ -31,7 +31,7 @@ QDLDL
 OSQP comes with `QDLDL <https://github.com/oxfordcontrol/qdldl>`_ internally installed.
 It does not require any external shared library.
 QDLDL is a sparse direct solver that works well for most small to medium sized problems.
-However, its becomes not really efficient for large scale problems since it is not multi-threaded.
+However, it becomes not really efficient for large scale problems since it is not multi-threaded.
 
 
 MKL Pardiso
@@ -61,7 +61,7 @@ Install with Anaconda
 ^^^^^^^^^^^^^^^^^^^^^
 `Anaconda Python distribution <https://www.anaconda.com/download/>`_ comes with the intel MKL libraries preinstalled including MKL Pardiso.
 To use this version, the Anaconda libraries folders have to be in your system path.
-Anaconda environments should add them automatically so in most cases you do not have do to anything. If you get an error where OSQP cannot find MKL, you can add the right path by adding the output from the following command to your path variable:
+Anaconda environments should add them automatically so in most cases you do not have to do anything. If you get an error where OSQP cannot find MKL, you can add the right path by adding the output from the following command to your path variable:
 
 .. code::
 

diff --git a/docs/index.rst b/docs/index.rst
@@ -1,6 +1,6 @@
 OSQP solver documentation
 ==========================
-**Join our** `forum <https://groups.google.com/forum/#!forum/osqp>`_ **for any
+**Join our** `forum <https://osqp.discourse.group/>`_ **for any
 questions related to the solver!**
 
 The OSQP (Operator Splitting Quadratic Program) solver is a numerical
@@ -70,11 +70,11 @@ OSQP is distributed under the `Apache 2.0 License <https://www.apache.org/licens
 The following people have been involved in the development of OSQP:
 
 * `Bartolomeo Stellato <https://stellato.io/>`_ (MIT): main development
-* `Goran Banjac <http://people.ee.ethz.ch/~gbanjac/>`_ (ETH Zürich): main development
-* `Nicholas Moehle <http://web.stanford.edu/~moehle/>`_ (Stanford University): methods, maths, and code generation
+* `Goran Banjac <https://people.ee.ethz.ch/~gbanjac/>`_ (ETH Zürich): main development
+* `Nicholas Moehle <https://www.nicholasmoehle.com/>`_ (Stanford University): methods, maths, and code generation
 * `Paul Goulart <http://users.ox.ac.uk/~engs1373/>`_ (University of Oxford): methods, maths, and Matlab interface
 * `Alberto Bemporad <http://cse.lab.imtlucca.it/~bemporad/>`_ (IMT Lucca): methods and maths
-* `Stephen Boyd <http://web.stanford.edu/~boyd/>`_ (Stanford University): methods and maths
+* `Stephen Boyd <https://web.stanford.edu/~boyd/>`_ (Stanford University): methods and maths
 
 Interfaces development
 

diff --git a/docs/parsers/jump.rst b/docs/parsers/jump.rst
@@ -1,7 +1,7 @@
 JuMP
 =====
 
-`JuMP <https://github.com/JuliaOpt/JuMP.jl>`_ supports the OSQP solver using the `MathProgBase interface <https://github.com/JuliaOpt/MathProgBase.jl>`_. 
+`JuMP <https://github.com/JuliaOpt/JuMP.jl>`_ supports the OSQP solver using the `MathOptInterface interface <https://github.com/JuliaOpt/MathOptInterface.jl>`_. 
 You can define a JuMP model to be solved via OSQP as follows
 
 
@@ -26,4 +26,4 @@ After defining your model, you can solve it by just calling
    solve(model)
 
 
-For more details on how to create and modify the models, see the `JuMP Documentation <https://github.com/JuliaOpt/JuMP.jl>`_.
+For more details on how to create and modify the models, see the `JuMP Documentation <https://www.juliaopt.org/JuMP.jl/stable/>`_.
diff --git a/docs/parsers/yalmip.rst b/docs/parsers/yalmip.rst
@@ -1,7 +1,7 @@
 YALMIP
 ======
 
-`YALMIP <https://yalmip.github.io/download/>`_ support the OSQP solver. You can easily define problems in high-level format and then specify OSQP by simply setting
+`YALMIP <https://yalmip.github.io/>`_ support the OSQP solver. You can easily define problems in high-level format and then specify OSQP by simply setting
 
 .. code:: matlab
 

diff --git a/docs/solver/index.rst b/docs/solver/index.rst
@@ -16,8 +16,7 @@ where :math:`x\in\mathbf{R}^{n}` is the optimization variable.
 The objective function is defined by a positive semidefinite matrix
 :math:`P \in \mathbf{S}^{n}_{+}` and vector :math:`q\in \mathbf{R}^{n}`.
 The linear constraints are defined by matrix :math:`A\in\mathbf{R}^{m \times n}`
-and vectors :math:`l \in \mathbf{R}^{m} \cup \{-\infty\}^{m}`,
-:math:`u \in \mathbf{R}^{m} \cup \{+\infty\}^{m}`.
+and vectors :math:`l` and :math:`u` so that :math:`l_i \in \mathbf{R} \cup \{-\infty\}` and :math:`u_i \in \mathbf{R} \cup \{+\infty\}` for all :math:`i \in \{1,\ldots,m\}`.
 
 
 Algorithm

diff --git a/include/osqp_api_constants.h b/include/osqp_api_constants.h
@@ -98,7 +98,7 @@ extern const char * OSQP_ERROR_MESSAGE[];
 # endif /* ifndef OSQP_NAN */
 
 # ifndef OSQP_INFTY
-#  define OSQP_INFTY ((c_float)1e20)        // infinity
+#  define OSQP_INFTY ((c_float)1e30)        // infinity
 # endif /* ifndef OSQP_INFTY */
 
 

diff --git a/lin_sys/direct/qdldl/qdldl_interface.c b/lin_sys/direct/qdldl/qdldl_interface.c
@@ -61,14 +61,21 @@ static c_int LDL_factor(csc *A,  qdldl_solver * p, c_int nvar){
     if (sum_Lnz < 0){
       // Error
 #ifdef PRINTING
-      c_eprint("Error in KKT matrix LDL factorization when computing the elimination tree. A is not perfectly upper triangular");
+      c_eprint("Error in KKT matrix LDL factorization when computing the elimination tree.");
+      if(sum_Lnz == -1){
+        c_eprint("Matrix is not perfectly upper triangular.");
+      }
+      else if(sum_Lnz == -2){
+        c_eprint("Integer overflow in L nonzero count.");
+      }
 #endif
       return sum_Lnz;
     }
 
     // Allocate memory for Li and Lx
     p->L->i = (c_int *)c_malloc(sizeof(c_int)*sum_Lnz);
     p->L->x = (c_float *)c_malloc(sizeof(c_float)*sum_Lnz);
+    p->L->nzmax = sum_Lnz;
 
     // Factor matrix
     factor_status = QDLDL_factor(A->n, A->p, A->i, A->x,

diff --git a/lin_sys/direct/qdldl/qdldl_sources b/lin_sys/direct/qdldl/qdldl_sources
diff --git a/src/auxil.c b/src/auxil.c
@@ -743,8 +743,8 @@ c_int check_termination(OSQPSolver *solver, c_int approximate) {
   eps_dual_inf = settings->eps_dual_inf;
 
   // If residuals are too large, the problem is probably non convex
-  if ((info->pri_res > 2 * OSQP_INFTY) ||
-      (info->dua_res > 2 * OSQP_INFTY)){
+  if ((info->pri_res > OSQP_INFTY) ||
+      (info->dua_res > OSQP_INFTY)){
     // Looks like residuals are diverging. Probably the problem is non convex!
     // Terminate and report it
     update_status(info, OSQP_NON_CVX);

diff --git a/src/util.c b/src/util.c
@@ -124,8 +124,10 @@ void print_setup_header(const OSQPSolver *solver) {
   if (settings->polish) c_print("polish: on, ");
   else c_print("polish: off, ");
 
+# ifdef PROFILING
   if (settings->time_limit) c_print("time_limit: %.2e sec\n", settings->time_limit);
   else c_print("time_limit: off\n");
+# endif /* ifdef PROFILING */
 
   c_print("\n");
 }