Language Reference |
LCP Call |
The LCP subroutine solves the linear complementarity problem:
That is, given a matrix and a vector , the LCP subroutine compute orthogonal, nonnegative vectors and which satisfy the previous equations.
The input arguments to the LCP subroutine are as follows:
is an matrix.
is an matrix.
is a scalar that defines virtual zero. The default value of epsilon is 1E8.
The LCP subroutine returns the following matrices:
returns one of the following scalar return codes:
solution found
no solution possible
solution is numerically unstable
subroutine could not obtain enough memory
returns an -element column vector
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;
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