cl1norm: A C++ repository from jnz

CL1NORM: Simplex L1 Solver Function

Based on the extremely well done and valuable work of Barrodale and Roberts:

I. Barrodale and F. D. K. Roberts. 1980.
Algorithm 552:
Solution of the Constrained L1 Linear Approximation Problem [F4].
ACM Trans. Math. Softw. 6, 2 (June 1980), 231-235.

This module, written in C (with a C++ wrapper), is compiled to a MATLAB .mex module cl1norm to solve linear programming problems in the following form:

A*x = b

A is a known design matrix, b is a known vector (e.g. from measurements). The vector x is the resulting L1 solution calculated by the cl1norm function:

x = cl1norm(A, b);

The sum of the absolute values of the residuals is minimized (L1 norm):

min. Σ( |b - A*x| )

Optionally linear constraints and linear inequality constraints can be specified:

C*x = d

and

E*x <= f

The vector x is then calculated by the cl1norm function:

x = cl1norm(A, b, C, d, E, f);

Speed

Solving some linear programming problems can be challenging in terms of worst-case execution time (WCET) and numerical stability. This algorithm might not be suitable for every application. In my experience though the algorithm is very solid and fast for quite a wide range of use cases.

A single data point on the performance on my desktop computer, comparing MATLAB's quadprog vs. cl1norm:

Solving a Model Predictive Control (MPC) problem with MATLAB's quadprog (Quadratic Programming) takes about 1 ms:

Elapsed time is 0.001047 seconds.

Solving the same MPC problem with cl1norm (Linear Programming) with the same prediction horizon:

Elapsed time is 0.000012 seconds.

Re-build MATLAB Mex file:

Pre-compiled binaries for Windows and Linux are available in the build/ folder.

To re-build from source, type in the MATLAB console:

mex -O ./src/cl1norm.cpp

On Linux the following script might help to compile the .mex file:

./mexcompile_linux.sh

Details on the cl1norm(...) MATLAB command

Usage: [x, res, info] = cl1norm(A, B, C, D, E, F, tol, maxiter);

Input: A, B, C (optional), D (optional), E (optional), F (optional),
tolerance (optional), maxiter (optional).
A*x = B, C*x = D, E*x <= F
C and D can be empty matrices [] and/or E and F can be empty matrices [].
tolerance: A small positive tolerance. Default: 1e-9.
maxiter: Maximum number of iterations for the algorithm.
Output: x, residuals (optional), simplexinfo (optional)
simplexinfo(1): 0 - optimal solution found. >=1 no solution found.
simplexinfo(2): Minimum sum of absolute values of the residuals.
simplexinfo(3): Number of simplex iterations.

C++ environment

The function can be used in a C++ program with the Eigen math library (link: https://eigen.tuxfamily.org/).

For Eigen::Matrix<double> the following function can be used:

int cl1_double(const Matrix<double, Dynamic, Dynamic>& A,
               const Matrix<double, Dynamic, 1>& B,
               Matrix<double, Dynamic, 1>& X,
               const Matrix<double, Dynamic, Dynamic>* C,
               const Matrix<double, Dynamic, 1>* D,
               const Matrix<double, Dynamic, Dynamic>* E,
               const Matrix<double, Dynamic, 1>* F,
               ... )

and for Eigen::Matrix<float>:

int cl1_float(const Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic>& A,
              const Eigen::Matrix<float, Eigen::Dynamic, 1>& B,
              Eigen::Matrix<float, Eigen::Dynamic, 1>& X,
              const Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic>* C,
              const Eigen::Matrix<float, Eigen::Dynamic, 1>* D,
              const Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic>* E,
              const Eigen::Matrix<float, Eigen::Dynamic, 1>* F,
              ... )

Note: optional input arguments (C,D,E,F) are handed over as pointers and can be set to NULL.

C environment

The basic functionality is in the cl1 function that can be extracted from cl1norm.cpp and embedded into a pure C project.

void cl1(const int *k, const int *l, const int *m,
         const int *n, const int *klmd, const int *klm2d,
         const int *nklmd, const int *n2d, real *q,
         int *kode, const real *toler, int *iter, real *x,
         real *res, real *error, real *cu, int *iu, int *s)
{
    ...
}

Note that a real typedef is used here that can be setup according to project needs, e.g.:

typedef double real;

The A, b, C, d, E and f matrices are collected in a single q matrix: