A tiny linear program solver library.
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lp_tiny - A tiny library to solve linear programs.

=== Description ===

A linear program is an optimization problem of the following type:

  minimize    c^T * x       (the linear objective function)
  subject to  A*x == b      (the set of equality constraints)
              x >= 0        (the set of positivity constraints)
where x is dimension n, and A has m rows, with n > m, and rank(A) == m.

lp_tiny solves these kinds of problems under the additional restriction
that the sublevel sets of the problem are bounded (that is, for a given
value of the objective function, the constraints produce a bounded region

This is a library written for programmers and mathematicians. We do not
coddle you with notions of individual variables or costs or other silly
notions from Operations Research proper. You are expected to be able to
express your problem in the mathematical form stated above.

=== Usage ===

First, your problem needs to be transformed to the above standard form.
In general, you will have general inequalities: A*x >= b or A*x <= b,
as well as unbounded variables that appear in the objective function.

For each general equality constraint, add slack variables which
represent the (positive) difference between the constraint and the
        A*x >= b    ---->     A*x - slack == b, slack >= 0
        A*x <= b    ---->     A*x + slack == b, slack >= 0

For each unbounded variable, split it into positive and negative parts:
               y    ---->     yp >= 0, ym >= 0  (y = yp - ym)
where the variable y is now eliminated and replaced by yp and ym, and
of course you have to modify the objective function's c appropriately.

Finally, you should check that your A matrix has full row rank. If not,
you can manually eliminate the linearly dependent rows beforehand (for
example, using a rank revealing QR decomposition). This is rarely a

Now you are ready to call the library. The interface is pretty simple:

    lp_tiny lp;              // linear program structure (see header)
    lp_tiny_status status;   // status of solution
    double x[2];             // solution vector
    lp_tiny_init(&lp, 2, 1); // all matrices and vectors already zeroed
    // Set objective function
    lp.c[0] = 1;
    lp.c[1] = 2;
    // Set constraints
    lp.A[0] = 1;
    lp.A[1] = 1;
    lp.b[0] = 1;
    // Perform solve
    lp_tiny_solve(&lp, x, NULL, NULL, &status, NULL);
    printf("Status: %s\n", lp_tiny_status_string(status));
    printf("Solution: %g, %g\n", x[0], x[1]);
    // Cleanup

If you get that your problem is unbounded when you know it should have a
finite solution, then it is probably because the sublevel sets of your
problem are not bounded. A simple way is to bound the sublevel sets is
to add a constraint row in the A matrix of all 1's, which places a limit
on the sum of all your unknowns. Of course, you should use extra
information about your problem to intelligently place bounds on individual
variables if possible.

The library is composed of just 3 files:
only one of which is a source file. The code depends on LAPACK, but I will
supply basic C implementations of the necessary functions in the future.

=== Performance ===

There's actually nothing tiny about the size of problems lp_tiny can solve,
although it is limited to dense problems (if your matrix A is sparse, then
you can get huge speedups using more sophisticated methods).

On my rather modest test machine with reference BLAS, times in seconds:

   m:   1    2    4    8   16   32   64   128  256  512
n:   +--------------------------------------------------
2    |  0
4    |  0    0
8    |  0    0    0
16   |  0    0    0    0
32   |  0    0    0    0    0
64   |  0    0    0    0   0.01 0.02
128  |  0   0.01  0   0.01 0.01 0.04 0.14
256  | 0.01 0.01 0.01 0.02 0.03 0.08 0.28 0.97
512  | 0.01 0.01 0.02 0.03 0.07 0.16 0.55 3.70 16.3
1024 | 0.04 0.04 0.05 0.07 0.15 0.34 2.08 8.99 62.8 252

With optimized GotoBLAS, the 1024x512 case is solved in 29 seconds.

=== Tweaking ===

I have no idea how robust this library is. I have used it for a few random
things, and it appears to work alright. The code was originally written for
the final homework of a class on convex optimization, so it is quite a
textbook implementation. However, if anything goes wrong, the code is so
simple that you can just go in and modify or tweak things. Lots of iteration
limits are hard coded. Memory allocation routines can be modified, etc.