SCS

SCS (splitting conic solver) is a numerical optimization package for solving large-scale convex cone problems, based on our paper Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding. It is written in C and can be used in other C, C++, Python, Matlab, R, Julia, programs via the linked interfaces. It can also be called as a solver from convex optimization toolboxes CVX (3.0 or later), CVXPY, Convex.jl, and Yalmip.

The current version is 2.1.1. If you wish to cite SCS, please use the following:

@article{ocpb:16,
    author       = {B. O'Donoghue and E. Chu and N. Parikh and S. Boyd},
    title        = {Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding},
    journal      = {Journal of Optimization Theory and Applications},
    month        = {June},
    year         = {2016},
    volume       = {169},
    number       = {3},
    pages        = {1042-1068},
    url          = {http://stanford.edu/~boyd/papers/scs.html},
}
@misc{scs,
    author       = {B. O'Donoghue and E. Chu and N. Parikh and S. Boyd},
    title        = {{SCS}: Splitting Conic Solver, version 2.1.1},
    howpublished = {\url{https://github.com/cvxgrp/scs}},
    month        = nov,
    year         = 2017
}

SCS numerically solves convex cone programs using the alternating direction method of multipliers (ADMM). It returns solutions to both the primal and dual problems if the problem is feasible, or a certificate of infeasibility otherwise. It solves the following primal cone problem:

minimize        c'x
subject to      Ax + s = b
                s in K

over variables x and s, where A, b and c are user-supplied data and K is a user-defined convex cone. The dual problem is given by

maximize        -b'y
subject to      -A'y == c
                y in K^*

over variable y, where K^* denotes the dual cone to K.

The cone K can be any Cartesian product of the following primitive cones:

zero cone {x | x = 0 } (dual to the free cone {x | x in R})
positive orthant {x | x >= 0}
second-order cone {(t,x) | ||x||_2 <= t}
positive semidefinite cone { X | min(eig(X)) >= 0, X = X^T }
exponential cone {(x,y,z) | y e^(x/y) <= z, y>0 }
dual exponential cone {(u,v,w) | −u e^(v/u) <= e w, u<0}
power cone {(x,y,z) | x^a * y^(1-a) >= |z|, x>=0, y>=0}
dual power cone {(u,v,w) | (u/a)^a * (v/(1-a))^(1-a) >= |w|, u>=0, v>=0}

The rows of the data matrix A correspond to the cones in K. The rows of A must be in the order of the cones given above, i.e., first come the rows that correspond to the zero/free cones, then those that correspond to the positive orthants, then SOCs, etc. For a k dimensional semidefinite cone when interpreting the rows of the data matrix A SCS assumes that the k x k matrix variable has been vectorized by scaling the off-diagonal entries by sqrt(2) and stacking the lower triangular elements column-wise to create a vector of length k(k+1)/2. See the section on semidefinite programming below.

At termination SCS returns solution (x*, s*, y*) if the problem is feasible, or a certificate of infeasibility otherwise. See here for more details about cone programming and certificates of infeasibility.

Anderson Acceleration

By default SCS uses Anderson acceleration (AA) to speed up convergence. The number of iterates that SCS uses in the AA calculation can be controlled by the parameter acceleration_lookback in the settings struct. It defaults to 10. AA is available as a standalone package here. More details are available in our paper on AA here.

Semidefinite Programming

SCS assumes that the matrix variables and the input data corresponding to semidefinite cones have been vectorized by scaling the off-diagonal entries by sqrt(2) and stacking the lower triangular elements column-wise. For a k x k matrix variable (or data matrix) this operation would create a vector of length k(k+1)/2. Scaling by sqrt(2) is required to preserve the inner-product.

To recover the matrix solution this operation must be inverted on the components of the vector returned by SCS corresponding to semidefinite cones. That is, the off-diagonal entries must be scaled by 1/sqrt(2) and the upper triangular entries are filled in by copying the values of lower triangular entries.

More explicitly, we want to express Tr(C X) as vec(C)'*vec(X), where the vec operation takes the k x k matrix

X = [ X11 X12 ... X1k
      X21 X22 ... X2k
      ...
      Xk1 Xk2 ... Xkk ]

and produces a vector consisting of

vec(X) = (X11, sqrt(2)*X21, ..., sqrt(2)*Xk1, X22, sqrt(2)*X32, ..., Xkk).

Linear equation solvers

Each iteration of SCS requires the solution of a set of linear equations. This package includes two implementations for solving linear equations: a direct solver which uses a cached LDL factorization and an indirect solver based on conjugate gradients. The indirect solver can be run on either the cpu or gpu.

The direct solver uses external numerical linear algebra packages:

QDLDL
AMD.

Using SCS in C

Typing make at the command line will compile the code and create SCS libraries in the out folder. To run the tests execute:

make
make test
test/run_tests

If make completes successfully, it will produce two static library files, libscsdir.a, libscsindir.a, and two dynamic library files libscsdir.ext, libscsindir.ext (where .ext extension is platform dependent) in the same folder. It will also produce two demo binaries in the out folder named demo_socp_direct, and demo_socp_indirect. If you have a GPU and have CUDA installed, you can also execture make gpu to compile SCS to run on the GPU which will create additional libraries and demo binaries in the out folder corresponding to the gpu version. Note that the GPU version requires 32 bit ints, which can be enforced by compiling with DLONG=0.

To use the libraries in your own source code, compile your code with the linker option -L(PATH_TO_SCS_LIBS) and -lscsdir or -lscsindir (as needed). The API and required data structures are defined in the file include/scs.h. The four main API functions are:

ScsWork * scs_init(const ScsData * d, const ScsCone * k, ScsInfo * info);

This initializes the ScsWork struct containing the workspace that scs will use, and performs the necessary preprocessing (e.g. matrix factorization). All inputs d, k, and info must be memory allocated before calling.
scs_int scs_solve(ScsWork * w, const ScsData * d, const ScsCone * k, ScsSolution * sol, ScsInfo * info);

This solves the problem as defined by ScsData d and ScsCone k using the workspace in w. The solution is returned in sol and information about the solve is returned in info (outputs must have memory allocated before calling). None of the inputs can be NULL. You can call scs_solve many times for one call to scs_init, so long as the matrix A does not change (vectors b and c can change).
void scs_finish(ScsWork * w);

Called after all solves completed to free allocated memory and other cleanup.
scs_int scs(const ScsData * d, const ScsCone * k, ScsSolution * sol, ScsInfo * info);

Convenience method that simply calls all the above routines in order, for cases where the workspace does not need to be reused. All inputs must have memory allocated before this call.

The data matrix A is specified in column-compressed format and the vectors b and c are specified as dense arrays. The solutions x (primal), s (slack), and y (dual) are returned as dense arrays. Cones are specified as the struct defined in include/scs.h, the rows of A must correspond to the cones in the exact order as specified by the cone struct (i.e. put linear cones before second-order cones etc.).

Warm-start

You can warm-start SCS (supply a guess of the solution) by setting warm_start in the ScsData struct to 1 and supplying the warm-starts in the ScsSolution struct (x,y, and s). All inputs must be warm-started if any one is. These are used to initialize the iterates in scs_solve.

Re-using matrix factorization

If using the direct version you can factorize the matrix once and solve many times. Simply call scs_init once, and use scs_solve many times with the same workspace, changing the input data b and c (and optionally warm-starts) for each iteration.

Using your own linear system solver

To use your own linear system solver simply implement all the methods and the two structs in include/linsys.h and plug it in.

BLAS / LAPACK install error

If you get an error like cannot find -lblas or cannot find -llapack, then you need to install blas and lapack and / or update your environment variables to point to the install locations.

epatters/scs

SCS

Using SCS in C