The LCP subroutine solves the linear complementarity problem:

That is, given a matrix and a vector , the LCP subroutine computes orthogonal, nonnegative vectors and which satisfy the previous equations.

The input arguments to the LCP subroutine are as follows:

m

is an matrix.

q

is an matrix.

epsilon

is a scalar that defines virtual zero. The default value of epsilon is 1E8.

The LCP subroutine returns the following matrices:

rc

returns one of the following scalar return codes:

rc	Termination
0	A solution is found.
1	No solution is possible.
5	The solution is numerically unstable.
6	The subroutine could not obtain enough memory.

w

returns an -element column vector

z

returns an -element column vector

The following statements give a simple example:

q = {1, 1};
m = {1 0,
     0 1};
call lcp(rc, w, z, m, q);
print rc, w, z;

The next example shows the relationship between quadratic programming and the linear complementarity problem. Consider the linearly constrained quadratic program:

If is positive semidefinite, then a solution to the Kuhn-Tucker conditions solves QP. The Kuhn-Tucker conditions for QP are

In the linear complementarity problem, let

Then the Kuhn-Tucker conditions are expressed as finding and that satisfy

From the solution and to this linear complementarity problem, the solution to QP is obtained; namely, is the primal structural variable, the surpluses, and and are the dual variables. Consider a quadratic program with the following data:

This problem is solved by using the LCP subroutine as follows:

/*---- Data for the Quadratic Program -----*/
c = {1, 2, 3, 4};
h = {100 10 1 0, 10 100 10 1, 1 10 100 10, 0 1 10 100};
g = {1 2 3 4, 10 20 30 40};
b = {1, 1};

/*----- Express the Kuhn-Tucker Conditions as an LCP ----*/
m = h || -g`;
m = m // (g || j(nrow(g),nrow(g),0));
q = c // -b;

/*----- Solve for a Kuhn-Tucker Point --------*/
call lcp(rc, w, z, m, q);

/*------ Extract the Solution to the Quadratic Program ----*/
x = z[1:nrow(h)];
print rc x;