Solves a sparse linear programming problem by an infeasible primal-dual interior-point method.

Synopsis

#include <imsl.h>

double *imsl_d_sparse_lin_prog (int m, int n, int nza, Imsl_d_sparse_elem a[], double b[], double c[], …, 0)

Required Arguments

int m   (Input)
Number of constraints.

int n   (Input)
Number of variables.

int nza   (Input)
Number of nonzero entries in the constraint matrix A.

Imsl_d_sparse_elem a[]   (Input)
An array of length nza containing the location and value of each nonzero coefficient in the constraint matrix A.

double b[]   (Input)
An array of length m containing the right-hand side of the constraints; if there are limits on both sides of the constraints, then b contains the lower limit of the constraints.

double c[]   (Input)
An array of length n containing containing the coefficients of the objective function.

Return Value

A pointer to an array of length n containing the solution x of the linear programming problem. To release this space, use imsl_free. If no solution can be computed, then NULL is returned.

Synopsis with Optional Arguments

#include <imsl.h>

double *imsl_d_sparse_lin_prog (int m, int n, int nza, Imsl_d_sparse_elem a[], double b[], double c[],

IMSL_CONSTR_TYPE, int irtype[],

IMSL_UPPER_LIMIT, double bu[],

IMSL_LOWER_BOUND, double xlb[],

IMSL_UPPER_BOUND, double xub[],

IMSL_OBJ_CONSTANT, double c0,

IMSL_MAX_ITERATIONS, int max_iterations,

IMSL_OPT_TOL, double opt_tol,

IMSL_PRINF_TOL, double prinf_tol,

IMSL_DLINF_TOL, double dlinf_tol,

IMSL_PRINT, int iprint,

IMSL_PRESOLVE, int presolve,

IMSL_CSC_FORMAT, int a_colptr[], int a_rowind[], double a_values[],

IMSL_TERMINATION_STATUS, int *status,

IMSL_OBJ, double *obj,

IMSL_ITERATION_COUNT, int *iterations,

IMSL_DUAL, double **y,

IMSL_DUAL_USER, double y[],

IMSL_PRIMAL_INFEAS, double *err_b, double *err_u,

IMSL_DUAL_INFEAS, double *err_c,

IMSL_CP_RATIO_SMALLEST, double *cp_smallest,

IMSL_CP_RATIO_LARGEST, double *cp_largest,

IMSL_RETURN_USER, double x[],

0)

Optional Arguments

IMSL_CONSTR_TYPE, int irtype[]   (Input)
An array of length m containing the types of general constraints in the matrix A. Let ri = ai1 x1 + … + ainxn. Then, the value of irtype[i] signifies the following:

 

irtype[i]	Constraint
0	ri = bi
1	ri ≤ bi
2	ri ≥ bi
3	bi ≤ ri ≤ bui
4	Ignore this constraint

 

            Note that irtype[i] = 3 should only be used for constraints i with both finite lower and finite upper bound.  For one-sided constraints, use irtype[i] = 1 or irtype[i] = 2. For free constraints, use irtype[i] = 4.
Default:  irtype = 0

IMSL_UPPER_LIMIT, double bu[]   (Input)
Array of length m containing the upper limit of the constraints that have both a lower and an upper bound. If such a constraint exists, then optional argument IMSL_CONSTR_TYPE must be used to define the type of the constraints.  If irtype[i]≠ 3, i.e. if constraint i is not two-sided, then the corresponding entry in bu, bu[i], is ignored.
Default: None of the constraints has an upper bound.

IMSL_LOWER_BOUND, double xlb[]   (Input)
An array of length n containing the lower bound on the variables. If there is no lower bound on a variable, then −1030 should be set as the lower bound.
Default: xlb = 0.

IMSL_UPPER_BOUND, double xub[]   (Input)
An array of length n containing the upper bound on the variables. If there is no upper bound on a variable, then 1030 should be set as the upper bound.
Default:  None of the variables has an upper bound.

IMSL_OBJ_CONSTANT, double c0   (Input)
Value of the constant term in the objective function.
Default:  c0 = 0.

IMSL_MAX_ITERATIONS, int max_iterations (Input)
The maximum number of iterations allowed for the primal-dual solver.
Default:  max_iterations = 200.

IMSL_OPT_TOL, double opt_tol (Input)
The relative optimality tolerance.
Default:  opt_tol = 1.0e-10.

IMSL_PRINF_TOL, double prinf_tol (Input)
The primal infeasibility tolerance.
Default:  prinf_tol = 1.0e-8.

IMSL_DLINF_TOL, double dlinf_tol (Input)
The dual infeasibility tolerance.
Default:  dlinf_tol = 1.0e-8.

IMSL_PRINT, int iprint (Input)
Printing option:

 

iprint	Action
0	No printing is performed.
1	Prints statistics on the LP problem and the solution process.

 

            Default:  iprint = 0.

IMSL_PRESOLVE, int presolve   (Input)
Presolve the LP problem in order to reduce the problem size or to detect infeasibility or unboundedness of the problem. Depending on the number of presolve techniques used different presolve levels can be chosen:

 

presolve	Description
0	No presolving.
1	Eliminate singleton rows
2	Additionally to 1, eliminate redundant (and forcing) rows.
3	Additionally to 2, eliminate dominated variables.
4	Additionally to 3, eliminate singleton columns.
5	Additionally to 4, eliminate doubleton rows.
6	Additionally to 5, eliminate aggregate columns.

 

            Default:  presolve = 0.

IMSL_CSC_FORMAT, int a_colptr[], int a_rowind[], double a_values[]   (Input)
Accept the constraint matrix A in Harwell-Boeing (CSC) format. See the main “Introduction” chapter of this manual for a discussion of this storage scheme.

     If this optional argument is used, then required argument a is ignored.

IMSL_TERMINATION_STATUS, int *status   (Output)
The termination status for the problem.

 

status	Description
0	Optimal solution found.
1	The problem is primal infeasible (or dual unbounded).
2	The problem is primal unbounded (or dual infeasible).
3	Suboptimal solution found (accuracy problems).
4	Iterations limit max_iterations exceeded.

 

IMSL_OBJ, double *obj   (Output)
Optimal value of the objective function.

IMSL_ITERATION_COUNT, int *iterations   (Output)
The number of iterations required by the primal-dual solver.

IMSL_DUAL, double **y   (Output)
The address of a pointer y to an internally allocated array of length m containing the dual solution.

IMSL_DUAL_USER, double y[]   (Output)
A user-allocated array of length m containing the dual solution..

IMSL_PRIMAL_INFEAS, double *err_b, double *err_u   (Output)
The violation of the primal constraints, described by err_b, the primal infeasibility of the solution, and by err_u, the violation of the variable bounds.

IMSL_DUAL_INFEAS, double *err_c   (Output)
The violation of the dual constraints, described by err_c, the dual infeasibility of the solution.

IMSL_CP_RATIO_SMALLEST, double *cp_smallest   (Output)
The ratio of the smallest complementarity product to the average.

IMSL_CP_RATIO_LARGEST, double *cp_largest   (Output)
The ratio of the largest complementarity product to the average.

IMSL_RETURN_USER, double x[]   (Output)
A user-allocated array of length n containing the primal solution.

Description

The function imsl_d_sparse_lin_prog uses an infeasible primal-dual interior-point method to solve linear programming problems, i.e., problems of the form

where c is the objective coefficient vector, A is the coefficient matrix, and the vectors bl, bu, xl, and xu are the lower and upper bounds on the constraints and the variables, respectively.

Internally, imsl_d_sparse_lin_prog transforms the problem given by the user into a simpler form that is computationally more tractable. After redefining the notation, the new form reads

Here, is a partition of the index set into upper bounded and standard variables.

In order to simplify the description it is assumed in the following that the problem above contains only variables with upper bounds, i.e. is of the form

The corresponding dual problem is then

The Karush-Kuhn-Tucker (KKT) optimality conditions for (P) and (D) are

where are diagonal matrices and  is a vector of ones.

Function imsl_d_sparse_lin_prog, like all infeasible interior point methods, generates a sequence

    

of iterates, that satisfy   for all , but are in general not feasible, i.e. the linear constraints (1.1)-(1.3) are only satisfied in the limiting case .

The barrier parameter , defined by

,

measures how good the complementarity conditions (1.4), (1.5) are satisfied.

Mehrotra’s predictor-corrector algorithm is a variant of Newton’s method applied to the KKT conditions (1.1)-(1.5). Function imsl_d_sparse_lin_prog uses a modified version of this algorithm to compute the iterates . In every step of the algorithm, the search direction vector

is decomposed into two parts, where  and denote the affine-scaling and a weighted centering component, respectively. Here,

,

where and denote the primal and dual corrector weights, respectively.

The vectors and are determined by solving the linear system

for two different right-hand sides.

For , the right-hand side is defined as

Here,  and  are the violations of the primal constraints and defines the violations of the dual constraints.

The resulting direction  is the pure Newton step applied to the system (1.1)-(1.5).

In order to obtain the corrector direction , the maximum stepsizes  in the primal and in the dual space preserving nonnegativity of and respectively, are determined and the predicted complementarity gap

is computed. It is then used to determine the barrier parameter

where  denotes the current complementarity gap.

The direction is then computed by choosing

as the right-hand side in the linear system (2).

Function imsl_d_sparse_lin_prog now uses a linesearch to find the optimal weight  that maximizes the stepsizes in the primal and dual directions of , respectively.

A new iterate is then computed using a step reduction factor :

In addition to the weighted Mehrotra predictor-corrector, imsl_d_sparse_lin_prog also uses multiple centrality correctors to enlarge the primal-dual stepsizes per iteration step and to reduce the overall number of iterations required to solve an LP problem. The maximum number of centrality corrections depends on the ratio of the factorization and solve efforts for system (2) and is therefore problem dependent. For a detailed description of multiple centrality correctors, refer to Gondzio(1994).

The linear system (2) can be reduced to more compact forms, the augmented system (AS)

or further by elimination of  to the normal equations (NE) system

where

,   ,   .

The matrix on the left-hand side of (3), which is symmetric indefinite, can be transformed into a symmetric quasidefinite matrix by regularization. Since these types of matrices allow for a Cholesky-like factorization, the resulting linear system can be solved easily for  by triangular substitutions. For more information on the regularization technique, see Altman and Gondzio (1998). For the NE system, matrix is positive definite, and therefore a sparse Cholesky algorithm can be used to factor and solve the system for by triangular substitutions with the Cholesky factor .

In function imsl_d_sparse_lin_prog, both approaches are implemented. The AS approach is chosen if  contains dense columns, if there is a considerable number of columns in  that are much denser than the remaining ones or if there are many more rows than columns in the structural part of . Otherwise, the NE approach is selected.

Function imsl_d_sparse_lin_prog stops with optimal termination status if the current iterate satisfies the following three conditions:

,

where prinf_tol, dlinf_tol and opt_tol are primal infeasibility, dual infeasibility and optimality tolerances, respectively. The default value is 1.0e-10 for opt_tol and 1.0e-8 for the two other tolerances.

Function imsl_d_sparse_lin_prog is based on the code HOPDM developed by Jacek Gondzio et al., see the HOPDM User’s Guide (1995).

Examples

Example 1

The linear programming problem

is solved.

 

#include <imsl.h>

#include <stdio.h>

 

int main()

{

    int m = 3, n = 3, nza = 7;

    double obj, *x = NULL;

 

    Imsl_d_sparse_elem a[] = { 0, 0, 1.0,

        0, 1, 3.0,

        1, 1, 2.0,

        1, 2, 3.0,

        2, 0, 1.0,

        2, 1, 1.0,

        2, 2, 1.0 };

 

    double b[] = { 3.0, 6.0, 2.0 };

    double c[] = { 2.0, -8.0, 3.0 };

    double xlb[] = { -1.0, 0.0, 0.0 };

    double xub[] = { 5.0, 7.0, 9.0 };

    int irtype[] = { 1, 1, 2 };

 

    x = imsl_d_sparse_lin_prog(m, n, nza, a, b, c,

        IMSL_CONSTR_TYPE, irtype,

        IMSL_LOWER_BOUND, xlb,

        IMSL_UPPER_BOUND, xub,

        IMSL_OBJ, &obj,

        0);

 

    imsl_d_write_matrix("x", 1, n, x, 0);

    printf("\nObjective: %lf\n", obj);

}

Output

                  x

          1            2            3

     -0.375        1.125        1.250

 

Objective: -6.000000

Example 2

This example demonstrates how the function imsl_d_read_mps can be used with imsl_d_sparse_lin_prog to solve a linear programming problem defined in an MPS file.  The MPS file used in this example is an uncompressed version of the file ‘afiro’, available from http://www.netlib.org/lp/data/.

 

#include <imsl.h>

#include <stdio.h>

#include <stdlib.h>

 

int main()

{

    Imsl_d_mps  *problem;

    int    i, m, n, *irtype, nza;

    double  *x, objective, *bl, *bu, *xlb, *xub;

    Imsl_d_sparse_elem *a = NULL;

 

    /* Read the MPS file. */

    problem = imsl_d_read_mps("afiro", 0);

 

    m = problem->nrows;

    n = problem->ncolumns;

 

    /*

    * Setup the constraint matrix.

    */

    nza = problem->nonzeros;

    a = problem->constraint;

 

    /*

    * Setup constraint bounds and constraint type array.

    */

    irtype = (int*) malloc(m*sizeof(int));

    bl = (double*) malloc(m*sizeof(double));

    bu = (double*) malloc(m*sizeof(double));

    for (i = 0; i < m; i++) {

        if (problem->lower_range[i] == problem->negative_infinity &&

            problem->upper_range[i] == problem->positive_infinity)

        {

            bl[i] = problem->negative_infinity;

            bu[i] = problem->positive_infinity;

            irtype[i] = 4;

        }

        else if (problem->lower_range[i] == problem->negative_infinity)

        {

            irtype[i] = 1;

            bl[i] = problem->upper_range[i];

            bu[i] = problem->positive_infinity;

        }

        else if (problem->upper_range[i] == problem->positive_infinity)

        {

            irtype[i] = 2;

            bl[i] = problem->lower_range[i];

            bu[i] = problem->positive_infinity;

        }

        else

        {

            if (problem->lower_range[i] == problem->upper_range[i])

            {

                irtype[i] = 0;

                bl[i] = problem->lower_range[i];

                bu[i] = problem->positive_infinity;

            }

            else

            {

                irtype[i] = 3;

                bl[i] = problem->lower_range[i];

                bu[i] = problem->upper_range[i];

            }

        }

    }

 

    /*

    * Setup variable bounds.  Be sure to account for

    * how unbounded variables should be set.

    */

    xlb = (double*) malloc(n*sizeof(double));

    xub = (double*) malloc(n*sizeof(double));

    for (i = 0; i < n; i++) {

        xlb[i] = (problem->lower_bound[i] == problem->negative_infinity)?

            -1.0e30:problem->lower_bound[i];

        xub[i] = (problem->upper_bound[i] == problem->positive_infinity)?

            1.0e30:problem->upper_bound[i];

    }

 

    /*

    * Solve the LP problem.

    */

    x = imsl_d_sparse_lin_prog(m, n, nza,

        a, bl, problem->objective,

        IMSL_UPPER_LIMIT, bu,

        IMSL_CONSTR_TYPE, irtype,

        IMSL_LOWER_BOUND, xlb,

        IMSL_UPPER_BOUND, xub,

        IMSL_OBJ, &objective,

        IMSL_PRESOLVE, 6,

        0);

 

    printf("Problem Name: %s\n", problem->name);

    printf("objective   : %15.10e\n", objective);

    imsl_d_write_matrix("Solution", 1, n, x, 0);

 

    /*

    * Free memory.

    */

    imsl_d_mps_free(problem);

    imsl_free(irtype);

    imsl_free(bl);

    imsl_free(bu);

    imsl_free(xlb);

    imsl_free(xub);

    imsl_free(x);

}

 

Output

 

Problem Name: AFIRO

objective   : -4.6475314284e+002

 

                                  Solution

          1            2            3            4            5            6

       80.0         25.5         54.5         84.8         65.4          0.0

 

          7            8            9           10           11           12

        0.0          0.0          0.0          0.0          0.0          0.0

 

         13           14           15           16           17           18

       18.2         47.2         69.4        500.0        475.9         24.1

 

         19           20           21           22           23           24

        0.0        215.0        141.7          0.0          0.0          0.0

 

         25           26           27           28           29           30

        0.0          0.0          0.0          0.0        339.9        242.3

 

         31           32

       60.9          0.0

 

Warning Errors

 

IMSL_ALL_FEAS_SOLS_OPTIMAL	The coefficients of the objective function are all equal to zero. Every feasible solution is also optimal.
IMSL_SUBOPTIMAL_SOL_FOUND	A suboptimal solution was found after # iterations.
IMSL_MAX_ITERATIONS_REACHED_1	The maximum number of iterations was reached. The best answer will be returned.  “#” = # was used, a larger value may help complete the algorithm.

 

Fatal Errors

 

IMSL_PRIMAL_UNBOUNDED	The primal problem is unbounded.
IMSL_PRIMAL_INFEASIBLE	The primal problem is infeasible.
IMSL_DUAL_INFEASIBLE	The dual problem is infeasible.
IMSL_INIT_SOL_INFEASIBLE	The initial solution for the one-row linear program is infeasible.
IMSL_PROB_UNBOUNDED	The problem is unbounded.
IMSL_DIAG_WEIGHT_TOO_SMALL	The diagonal element # [#] = # of the diagonal weight matrix # is too small.
IMSL_CHOL_FAC_ACCURACY	The Cholesky factorization failed because of accuracy problems.

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