| [usrmat,mrelas,nvars,costs,prgopt,dattrv,bl,bu,ind,info,primal,duals,ibasis,work,lw,iwork,liw]=splp(usrmat,mrelas,nvars,costs,prgopt,dattrv,bl,bu,ind,info,primal,duals,ibasis,work,lw,iwork,liw); |
function [usrmat,mrelas,nvars,costs,prgopt,dattrv,bl,bu,ind,info,primal,duals,ibasis,work,lw,iwork,liw]=splp(usrmat,mrelas,nvars,costs,prgopt,dattrv,bl,bu,ind,info,primal,duals,ibasis,work,lw,iwork,liw);
persistent iadbig ictmax ictopt iopt key lamat last lbasma lbm lcolnr lcsc lerd lerp libb librc limat lipr liwork liwr lmx lrg lrhs lrprim lrz lwork lwr lww nerr next xern1 xern2 zero ;
if isempty(iadbig), iadbig=0; end;
if isempty(ictmax), ictmax=0; end;
if isempty(ictopt), ictopt=0; end;
if isempty(iopt), iopt=0; end;
if isempty(key), key=0; end;
if isempty(lamat), lamat=0; end;
if isempty(last), last=0; end;
if isempty(lbasma), lbasma=0; end;
if isempty(lbm), lbm=0; end;
if isempty(lcolnr), lcolnr=0; end;
if isempty(lcsc), lcsc=0; end;
if isempty(lerd), lerd=0; end;
if isempty(lerp), lerp=0; end;
if isempty(libb), libb=0; end;
if isempty(librc), librc=0; end;
if isempty(limat), limat=0; end;
if isempty(lipr), lipr=0; end;
if isempty(liwork), liwork=0; end;
if isempty(liwr), liwr=0; end;
if isempty(lmx), lmx=0; end;
if isempty(lrg), lrg=0; end;
if isempty(lrhs), lrhs=0; end;
if isempty(lrprim), lrprim=0; end;
if isempty(lrz), lrz=0; end;
if isempty(lwork), lwork=0; end;
if isempty(lwr), lwr=0; end;
if isempty(lww), lww=0; end;
if isempty(nerr), nerr=0; end;
if isempty(next), next=0; end;
%***BEGIN PROLOGUE SPLP
%***PURPOSE Solve linear programming problems involving at
% most a few thousand constraints and variables.
% Takes advantage of sparsity in the constraint matrix.
%***LIBRARY SLATEC
%***CATEGORY G2A2
%***TYPE SINGLE PRECISION (SPLP-S, DSPLP-D)
%***KEYWORDS LINEAR CONSTRAINTS, LINEAR OPTIMIZATION,
% LINEAR PROGRAMMING, LP, SPARSE CONSTRAINTS
%***AUTHOR Hanson, R. J., (SNLA)
% Hiebert, K. L., (SNLA)
%***DESCRIPTION
%
% These are the short usage instructions; for details about
% other features, options and methods for defining the matrix
% A, see the extended usage instructions which are contained in
% the Long Description section below.
%
% |------------|
% |Introduction|
% |------------|
% The subprogram SPLP( ) solves a linear optimization problem.
% The problem statement is as follows
%
% minimize (transpose of costs)*x
% subject to A*x=w.
%
% The entries of the unknowns x and w may have simple lower or
% upper bounds (or both), or be free to take on any value. By
% setting the bounds for x and w, the user is imposing the con-
% straints of the problem. The matrix A has MRELAS rows and
% NVARS columns. The vectors costs, x, and w respectively
% have NVARS, NVARS, and MRELAS number of entries.
%
% The input for the problem includes the problem dimensions,
% MRELAS and NVARS, the array COSTS(*), data for the matrix
% A, and the bound information for the unknowns x and w, BL(*),
% BU(*), and IND(*). Only the nonzero entries of the matrix A
% are passed to SPLP( ).
%
% The output from the problem (when output flag INFO=1) includes
% optimal values for x and w in PRIMAL(*), optimal values for
% dual variables of the equations A*x=w and the simple bounds
% on x in DUALS(*), and the indices of the basic columns,
% IBASIS(*).
%
% |------------------------------|
% |Fortran Declarations Required:|
% |------------------------------|
%
% DIMENSION COSTS(NVARS),PRGOPT(*),DATTRV(*),
% *BL(NVARS+MRELAS),BU(NVARS+MRELAS),IND(NVARS+MRELAS),
% *PRIMAL(NVARS+MRELAS),DUALS(MRELAS+NVARS),IBASIS(NVARS+MRELAS),
% *WORK(LW),IWORK(LIW)
%
% EXTERNAL USRMAT
%
% The dimensions of PRGOPT(*) and DATTRV(*) must be at least 1.
% The exact lengths will be determined by user-required options and
% data transferred to the subprogram USRMAT( ).
%
% The values of LW and LIW, the lengths of the arrays WORK(*)
% and IWORK(*), must satisfy the inequalities
%
% LW .GE. 4*NVARS+ 8*MRELAS+LAMAT+ LBM
% LIW.GE. NVARS+11*MRELAS+LAMAT+2*LBM
%
% It is an error if they do not both satisfy these inequalities.
% (The subprogram will inform the user of the required lengths
% if either LW or LIW is wrong.) The values of LAMAT and LBM
% nominally are
%
% LAMAT=4*NVARS+7
% and LBM =8*MRELAS
%
% LAMAT determines the length of the sparse matrix storage area.
% The value of LBM determines the amount of storage available
% to decompose and update the active basis matrix.
%
% |------|
% |Input:|
% |------|
%
% MRELAS,NVARS
% ------------
% These parameters are respectively the number of constraints (the
% linear relations A*x=w that the unknowns x and w are to satisfy)
% and the number of entries in the vector x. Both must be .GE. 1.
% Other values are errors.
%
% COSTS(*)
% --------
% The NVARS entries of this array are the coefficients of the
% linear objective function. The value COSTS(J) is the
% multiplier for variable J of the unknown vector x. Each
% entry of this array must be defined.
%
% USRMAT
% ------
% This is the name of a specific subprogram in the SPLP( ) package
% used to define the matrix A. In this usage mode of SPLP( )
% the user places the nonzero entries of A in the
% array DATTRV(*) as given in the description of that parameter.
% The name USRMAT must appear in a Fortran EXTERNAL statement.
%
% DATTRV(*)
% ---------
% The array DATTRV(*) contains data for the matrix A as follows:
% Each column (numbered J) requires (floating point) data con-
% sisting of the value (-J) followed by pairs of values. Each pair
% consists of the row index immediately followed by the value
% of the matrix at that entry. A value of J=0 signals that there
% are no more columns. The required length of
% DATTRV(*) is 2*no. of nonzeros + NVARS + 1.
%
% BL(*),BU(*),IND(*)
% ------------------
% The values of IND(*) are input parameters that define
% the form of the bounds for the unknowns x and w. The values for
% the bounds are found in the arrays BL(*) and BU(*) as follows.
%
% For values of J between 1 and NVARS,
% if IND(J)=1, then X(J) .GE. BL(J); BU(J) is not used.
% if IND(J)=2, then X(J) .LE. BU(J); BL(J) is not used.
% if IND(J)=3, then BL(J) .LE. X(J) .LE. BU(J),(BL(J)=BU(J) ok)
% if IND(J)=4, then X(J) is free to have any value,
% and BL(J), BU(J) are not used.
%
% For values of I between NVARS+1 and NVARS+MRELAS,
% if IND(I)=1, then W(I-NVARS) .GE. BL(I); BU(I) is not used.
% if IND(I)=2, then W(I-NVARS) .LE. BU(I); BL(I) is not used.
% if IND(I)=3, then BL(I) .LE. W(I-NVARS) .LE. BU(I),
% (BL(I)=BU(I) is ok).
% if IND(I)=4, then W(I-NVARS) is free to have any value,
% and BL(I), BU(I) are not used.
%
% A value of IND(*) not equal to 1,2,3 or 4 is an error. When
% IND(I)=3, BL(I) must be .LE. BU(I). The condition BL(I).GT.
% BU(I) indicates infeasibility and is an error.
%
% PRGOPT(*)
% ---------
% This array is used to redefine various parameters within SPLP( ).
% Frequently, perhaps most of the time, a user will be satisfied
% and obtain the solutions with no changes to any of these
% parameters. To try this, simply set PRGOPT(1)=1.0E0.
%
% For users with more sophisticated needs, SPLP( ) provides several
% options that may be used to take advantage of more detailed
% knowledge of the problem or satisfy other utilitarian needs.
% The complete description of how to use this option array to
% utilize additional subprogram features is found under the
% heading of SPLP( ) Subprogram Options in the Extended
% Usage Instructions.
%
% Briefly, the user should note the following value of the parameter
% KEY and the corresponding task or feature desired before turning
% to that document.
%
% Value Brief Statement of Purpose for Option
% of KEY
% ------ -------------------------------------
% 50 Change from a minimization problem to a
% maximization problem.
% 51 Change the amount of printed output.
% Normally, no printed output is obtained.
% 52 Redefine the line length and precision used
% for the printed output.
% 53 Redefine the values of LAMAT and LBM that
% were discussed above under the heading
% Fortran Declarations Required.
% 54 Redefine the unit number where pages of the sparse
% data matrix A are stored. Normally, the unit
% number is 1.
% 55 A computation, partially completed, is
% being continued. Read the up-to-date
% partial results from unit number 2.
% 56 Redefine the unit number where the partial results
% are stored. Normally, the unit number is 2.
% 57 Save partial results on unit 2 either after
% maximum iterations or at the optimum.
% 58 Redefine the value for the maximum number of
% iterations. Normally, the maximum number of
% iterations is 3*(NVARS+MRELAS).
% 59 Provide SPLP( ) with a starting (feasible)
% nonsingular basis. Normally, SPLP( ) starts
% with the identity matrix columns corresponding
% to the vector w.
% 60 The user has provided scale factors for the
% columns of A. Normally, SPLP( ) computes scale
% factors that are the reciprocals of the max. norm
% of each column.
% 61 The user has provided a scale factor
% for the vector costs. Normally, SPLP( ) computes
% a scale factor equal to the reciprocal of the
% max. norm of the vector costs after the column
% scaling for the data matrix has been applied.
% 62 Size parameters, namely the smallest and
% largest magnitudes of nonzero entries in
% the matrix A, are provided. Values noted
% outside this range are to be considered errors.
% 63 Redefine the tolerance required in
% evaluating residuals for feasibility.
% Normally, this value is set to RELPR,
% where RELPR = relative precision of the arithmetic.
% 64 Change the criterion for bringing new variables
% into the basis from the steepest edge (best
% local move) to the minimum reduced cost.
% 65 Redefine the value for the number of iterations
% between recalculating the error in the primal
% solution. Normally, this value is equal to ten.
% 66 Perform 'partial pricing' on variable selection.
% Redefine the value for the number of negative
% reduced costs to compute (at most) when finding
% a variable to enter the basis. Normally this
% value is set to NVARS. This implies that no
% 'partial pricing' is used.
% 67 Adjust the tuning factor (normally one) to apply
% to the primal and dual error estimates.
% 68 Pass information to the subprogram FULMAT(),
% provided with the SPLP() package, so that a Fortran
% two-dimensional array can be used as the argument
% DATTRV(*).
% 69 Pass an absolute tolerance to use for the feasibility
% test when the usual relative error test indicates
% infeasibility. The nominal value of this tolerance,
% TOLABS, is zero.
%
%
% |---------------|
% |Working Arrays:|
% |---------------|
%
% WORK(*),LW,
% IWORK(*),LIW
% ------------
% The arrays WORK(*) and IWORK(*) are respectively floating point
% and type INTEGER working arrays for SPLP( ) and its
% subprograms. The lengths of these arrays are respectively
% LW and LIW. These parameters must satisfy the inequalities
% noted above under the heading 'Fortran Declarations Required:'
% It is an error if either value is too small.
%
% |----------------------------|
% |Input/Output files required:|
% |----------------------------|
%
% Fortran unit 1 is used by SPLP( ) to store the sparse matrix A
% out of high-speed memory. A crude
% upper bound for the amount of information written on unit 1
% is 6*nz, where nz is the number of nonzero entries in A.
%
% |-------|
% |Output:|
% |-------|
%
% INFO,PRIMAL(*),DUALS(*)
% -----------------------
% The integer flag INFO indicates why SPLP( ) has returned to the
% user. If INFO=1 the solution has been computed. In this case
% X(J)=PRIMAL(J) and W(I)=PRIMAL(I+NVARS). The dual variables
% for the equations A*x=w are in the array DUALS(I)=dual for
% equation number I. The dual value for the component X(J) that
% has an upper or lower bound (or both) is returned in
% DUALS(J+MRELAS). The only other values for INFO are .LT. 0.
% The meaning of these values can be found by reading
% the diagnostic message in the output file, or by looking for
% error number = (-INFO) in the Extended Usage Instructions
% under the heading:
%
% List of SPLP( ) Error and Diagnostic Messages.
%
% BL(*),BU(*),IND(*)
% ------------------
% These arrays are output parameters only under the (unusual)
% circumstances where the stated problem is infeasible, has an
% unbounded optimum value, or both. These respective conditions
% correspond to INFO=-1,-2 or -3. See the Extended
% Usage Instructions for further details.
%
% IBASIS(I),I=1,...,MRELAS
% ------------------------
% This array contains the indices of the variables that are
% in the active basis set at the solution (INFO=1). A value
% of IBASIS(I) between 1 and NVARS corresponds to the variable
% X(IBASIS(I)). A value of IBASIS(I) between NVARS+1 and NVARS+
% MRELAS corresponds to the variable W(IBASIS(I)-NVARS).
%
% *Long Description:
%
% subroutine SPLP(USRMAT,MRELAS,NVARS,COSTS,PRGOPT,DATTRV,
% * BL,BU,IND,INFO,PRIMAL,DUALS,IBASIS,WORK,LW,IWORK,LIW)
%
% |------------|
% |Introduction|
% |------------|
% The subprogram SPLP( ) solves a linear optimization problem.
% The problem statement is as follows
%
% minimize (transpose of costs)*x
% subject to A*x=w.
%
% The entries of the unknowns x and w may have simple lower or
% upper bounds (or both), or be free to take on any value. By
% setting the bounds for x and w, the user is imposing the con-
% straints of the problem.
%
% (The problem may also be stated as a maximization
% problem. This is done by means of input in the option array
% PRGOPT(*).) The matrix A has MRELAS rows and NVARS columns. The
% vectors costs, x, and w respectively have NVARS, NVARS, and
% MRELAS number of entries.
%
% The input for the problem includes the problem dimensions,
% MRELAS and NVARS, the array COSTS(*), data for the matrix
% A, and the bound information for the unknowns x and w, BL(*),
% BU(*), and IND(*).
%
% The output from the problem (when output flag INFO=1) includes
% optimal values for x and w in PRIMAL(*), optimal values for
% dual variables of the equations A*x=w and the simple bounds
% on x in DUALS(*), and the indices of the basic columns in
% IBASIS(*).
%
% |------------------------------|
% |Fortran Declarations Required:|
% |------------------------------|
%
% DIMENSION COSTS(NVARS),PRGOPT(*),DATTRV(*),
% *BL(NVARS+MRELAS),BU(NVARS+MRELAS),IND(NVARS+MRELAS),
% *PRIMAL(NVARS+MRELAS),DUALS(MRELAS+NVARS),IBASIS(NVARS+MRELAS),
% *WORK(LW),IWORK(LIW)
%
% EXTERNAL USRMAT (or 'NAME', if user provides the subprogram)
%
% The dimensions of PRGOPT(*) and DATTRV(*) must be at least 1.
% The exact lengths will be determined by user-required options and
% data transferred to the subprogram USRMAT( ) ( or 'NAME').
%
% The values of LW and LIW, the lengths of the arrays WORK(*)
% and IWORK(*), must satisfy the inequalities
%
% LW .GE. 4*NVARS+ 8*MRELAS+LAMAT+ LBM
% LIW.GE. NVARS+11*MRELAS+LAMAT+2*LBM
%
% It is an error if they do not both satisfy these inequalities.
% (The subprogram will inform the user of the required lengths
% if either LW or LIW is wrong.) The values of LAMAT and LBM
% nominally are
%
% LAMAT=4*NVARS+7
% and LBM =8*MRELAS
%
% These values will be as shown unless the user changes them by
% means of input in the option array PRGOPT(*). The value of LAMAT
% determines the length of the sparse matrix 'staging' area.
% For reasons of efficiency the user may want to increase the value
% of LAMAT. The value of LBM determines the amount of storage
% available to decompose and update the active basis matrix.
% Due to exhausting the working space because of fill-in,
% it may be necessary for the user to increase the value of LBM.
% (If this situation occurs an informative diagnostic is printed
% and a value of INFO=-28 is obtained as an output parameter.)
%
% |------|
% |Input:|
% |------|
%
% MRELAS,NVARS
% ------------
% These parameters are respectively the number of constraints (the
% linear relations A*x=w that the unknowns x and w are to satisfy)
% and the number of entries in the vector x. Both must be .GE. 1.
% Other values are errors.
%
% COSTS(*)
% --------
% The NVARS entries of this array are the coefficients of the
% linear objective function. The value COSTS(J) is the
% multiplier for variable J of the unknown vector x. Each
% entry of this array must be defined. This array can be changed
% by the user between restarts. See options with KEY=55,57 for
% details of checkpointing and restarting.
%
% USRMAT
% ------
% This is the name of a specific subprogram in the SPLP( ) package
% that is used to define the matrix entries when this data is passed
% to SPLP( ) as a linear array. In this usage mode of SPLP( )
% the user gives information about the nonzero entries of A
% in DATTRV(*) as given under the description of that parameter.
% The name USRMAT must appear in a Fortran EXTERNAL statement.
% Users who are passing the matrix data with USRMAT( ) can skip
% directly to the description of the input parameter DATTRV(*).
% Also see option 68 for passing the constraint matrix data using
% a standard Fortran two-dimensional array.
%
% If the user chooses to provide a subprogram 'NAME'( ) to
% define the matrix A, then DATTRV(*) may be used to pass floating
% point data from the user's program unit to the subprogram
% 'NAME'( ). The content of DATTRV(*) is not changed in any way.
%
% The subprogram 'NAME'( ) can be of the user's choice
% but it must meet Fortran standards and it must appear in a
% Fortran EXTERNAL statement. The first statement of the subprogram
% has the form
%
% subroutine 'NAME'(I,J,AIJ, INDCAT, PRGOPT, DATTRV, IFLAG)
%
% The variables I,J, INDCAT, IFLAG(10) are type INTEGER,
% while AIJ, PRGOPT(*),DATTRV(*) are type REAL.
%
% The user interacts with the contents of IFLAG(*) to
% direct the appropriate action. The algorithmic steps are
% as follows.
%
% Test IFLAG(1).
%
% IF(IFLAG(1).EQ.1) THEN
%
% Initialize the necessary pointers and data
% for defining the matrix A. The contents
% of IFLAG(K), K=2,...,10, may be used for
% storage of the pointers. This array remains intact
% between calls to 'NAME'( ) by SPLP( ).
% RETURN
%
% endif
%
% IF(IFLAG(1).EQ.2) THEN
%
% Define one set of values for I,J,AIJ, and INDCAT.
% Each nonzero entry of A must be defined this way.
% These values can be defined in any convenient order.
% (It is most efficient to define the data by
% columns in the order 1,...,NVARS; within each
% column define the entries in the order 1,...,MRELAS.)
% If this is the last matrix value to be
% defined or updated, then set IFLAG(1)=3.
% (When I and J are positive and respectively no larger
% than MRELAS and NVARS, the value of AIJ is used to
% define (or update) row I and column J of A.)
% RETURN
%
% endif
%
% end
%
% Remarks: The values of I and J are the row and column
% indices for the nonzero entries of the matrix A.
% The value of this entry is AIJ.
% Set INDCAT=0 if this value defines that entry.
% Set INDCAT=1 if this entry is to be updated,
% new entry=old entry+AIJ.
% A value of I not between 1 and MRELAS, a value of J
% not between 1 and NVARS, or a value of INDCAT
% not equal to 0 or 1 are each errors.
%
% The contents of IFLAG(K), K=2,...,10, can be used to
% remember the status (of the process of defining the
% matrix entries) between calls to 'NAME'( ) by SPLP( ).
% On entry to 'NAME'( ), only the values 1 or 2 will be
% in IFLAG(1). More than 2*NVARS*MRELAS definitions of
% the matrix elements is considered an error because
% it suggests an infinite loop in the user-written
% subprogram 'NAME'( ). Any matrix element not
% provided by 'NAME'( ) is defined to be zero.
%
% The REAL arrays PRGOPT(*) and DATTRV(*) are passed as
% arguments directly from SPLP( ) to 'NAME'( ).
% The array PRGOPT(*) contains any user-defined program
% options. In this usage mode the array DATTRV(*) may
% now contain any (type REAL) data that the user needs
% to define the matrix A. Both arrays PRGOPT(*) and
% DATTRV(*) remain intact between calls to 'NAME'( )
% by SPLP( ).
% Here is a subprogram that communicates the matrix values for A,
% as represented in DATTRV(*), to SPLP( ). This subprogram,
% called USRMAT( ), is included as part of the SPLP( ) package.
% This subprogram 'decodes' the array DATTRV(*) and defines the
% nonzero entries of the matrix A for SPLP( ) to store. This
% listing is presented here as a guide and example
% for the users who find it necessary to write their own subroutine
% for this purpose. The contents of DATTRV(*) are given below in
% the description of that parameter.
%
% subroutine USRMAT(I,J,AIJ, INDCAT,PRGOPT,DATTRV,IFLAG)
% DIMENSION PRGOPT(*),DATTRV(*),IFLAG(10)
%
% IF(IFLAG(1).EQ.1) THEN
%
% THIS IS THE INITIALIZATION STEP. THE VALUES OF IFLAG(K),K=2,3,4,
% ARE RESPECTIVELY THE COLUMN INDEX, THE ROW INDEX (OR THE NEXT COL.
% INDEX), AND THE POINTER TO THE MATRIX ENTRY'S VALUE WITHIN
% DATTRV(*). ALSO CHECK (DATTRV(1)=0.) SIGNIFYING NO DATA.
% IF(DATTRV(1).EQ.0.) THEN
% I = 0
% J = 0
% IFLAG(1) = 3
% ELSE
% IFLAG(2)=-DATTRV(1)
% IFLAG(3)= DATTRV(2)
% IFLAG(4)= 3
% endif
%
% RETURN
% ELSE
% J=IFLAG(2)
% I=IFLAG(3)
% L=IFLAG(4)
% IF(I.EQ.0) THEN
%
% SIGNAL THAT ALL OF THE NONZERO ENTRIES HAVE BEEN DEFINED.
% IFLAG(1)=3
% RETURN
% ELSE IF(I.LT.0) THEN
%
% SIGNAL THAT A SWITCH IS MADE TO A NEW COLUMN.
% J=-I
% I=DATTRV(L)
% L=L+1
% endif
%
% AIJ=DATTRV(L)
%
% UPDATE THE INDICES AND POINTERS FOR THE NEXT ENTRY.
% IFLAG(2)=J
% IFLAG(3)=DATTRV(L+1)
% IFLAG(4)=L+2
%
% INDCAT=0 DENOTES THAT ENTRIES OF THE MATRIX ARE ASSIGNED THE
% VALUES FROM DATTRV(*). NO ACCUMULATION IS PERFORMED.
% INDCAT=0
% RETURN
% endif
% end
%
% DATTRV(*)
% ---------
% If the user chooses to use the provided subprogram USRMAT( ) then
% the array DATTRV(*) contains data for the matrix A as follows:
% Each column (numbered J) requires (floating point) data con-
% sisting of the value (-J) followed by pairs of values. Each pair
% consists of the row index immediately followed by the value
% of the matrix at that entry. A value of J=0 signals that there
% are no more columns. (See 'Example of SPLP( ) Usage,' below.)
% The dimension of DATTRV(*) must be 2*no. of nonzeros
% + NVARS + 1 in this usage. No checking of the array
% length is done by the subprogram package.
%
% If the Save/Restore feature is in use (see options with
% KEY=55,57 for details of checkpointing and restarting)
% USRMAT( ) can be used to redefine entries of the matrix.
% The matrix entries are redefined or overwritten. No accum-
% ulation is performed.
% Any other nonzero entry of A, defined in a previous call to
% SPLP( ), remain intact.
%
% BL(*),BU(*),IND(*)
% ------------------
% The values of IND(*) are input parameters that define
% the form of the bounds for the unknowns x and w. The values for
% the bounds are found in the arrays BL(*) and BU(*) as follows.
%
% For values of J between 1 and NVARS,
% if IND(J)=1, then X(J) .GE. BL(J); BU(J) is not used.
% if IND(J)=2, then X(J) .LE. BU(J); BL(J) is not used.
% if IND(J)=3, then BL(J) .LE. X(J) .LE. BU(J),(BL(J)=BU(J) ok)
% if IND(J)=4, then X(J) is free to have any value,
% and BL(J), BU(J) are not used.
%
% For values of I between NVARS+1 and NVARS+MRELAS,
% if IND(I)=1, then W(I-NVARS) .GE. BL(I); BU(I) is not used.
% if IND(I)=2, then W(I-NVARS) .LE. BU(I); BL(I) is not used.
% if IND(I)=3, then BL(I) .LE. W(I-NVARS) .LE. BU(I),
% (BL(I)=BU(I) is ok).
% if IND(I)=4, then W(I-NVARS) is free to have any value,
% and BL(I), BU(I) are not used.
%
% A value of IND(*) not equal to 1,2,3 or 4 is an error. When
% IND(I)=3, BL(I) must be .LE. BU(I). The condition BL(I).GT.
% BU(I) indicates infeasibility and is an error. These
% arrays can be changed by the user between restarts. See
% options with KEY=55,57 for details of checkpointing and
% restarting.
%
% PRGOPT(*)
% ---------
% This array is used to redefine various parameters within SPLP( ).
% Frequently, perhaps most of the time, a user will be satisfied
% and obtain the solutions with no changes to any of these
% parameters. To try this, simply set PRGOPT(1)=1.0E0.
%
% For users with more sophisticated needs, SPLP( ) provides several
% options that may be used to take advantage of more detailed
% knowledge of the problem or satisfy other utilitarian needs.
% The complete description of how to use this option array to
% utilize additional subprogram features is found under the
% heading 'Usage of SPLP( ) Subprogram Options.'
%
% Briefly, the user should note the following value of the parameter
% KEY and the corresponding task or feature desired before turning
% to that section.
%
% Value Brief Statement of Purpose for Option
% of KEY
% ------ -------------------------------------
% 50 Change from a minimization problem to a
% maximization problem.
% 51 Change the amount of printed output.
% Normally, no printed output is obtained.
% 52 Redefine the line length and precision used
% for the printed output.
% 53 Redefine the values of LAMAT and LBM that
% were discussed above under the heading
% Fortran Declarations Required.
% 54 Redefine the unit number where pages of the sparse
% data matrix A are stored. Normally, the unit
% number is 1.
% 55 A computation, partially completed, is
% being continued. Read the up-to-date
% partial results from unit number 2.
% 56 Redefine the unit number where the partial results
% are stored. Normally, the unit number is 2.
% 57 Save partial results on unit 2 either after
% maximum iterations or at the optimum.
% 58 Redefine the value for the maximum number of
% iterations. Normally, the maximum number of
% iterations is 3*(NVARS+MRELAS).
% 59 Provide SPLP( ) with a starting (feasible)
% nonsingular basis. Normally, SPLP( ) starts
% with the identity matrix columns corresponding
% to the vector w.
% 60 The user has provided scale factors for the
% columns of A. Normally, SPLP( ) computes scale
% factors that are the reciprocals of the max. norm
% of each column.
% 61 The user has provided a scale factor
% for the vector costs. Normally, SPLP( ) computes
% a scale factor equal to the reciprocal of the
% max. norm of the vector costs after the column
% scaling for the data matrix has been applied.
% 62 Size parameters, namely the smallest and
% largest magnitudes of nonzero entries in
% the matrix A, are provided. Values noted
% outside this range are to be considered errors.
% 63 Redefine the tolerance required in
% evaluating residuals for feasibility.
% Normally, this value is set to the value RELPR,
% where RELPR = relative precision of the arithmetic.
% 64 Change the criterion for bringing new variables
% into the basis from the steepest edge (best
% local move) to the minimum reduced cost.
% 65 Redefine the value for the number of iterations
% between recalculating the error in the primal
% solution. Normally, this value is equal to ten.
% 66 Perform 'partial pricing' on variable selection.
% Redefine the value for the number of negative
% reduced costs to compute (at most) when finding
% a variable to enter the basis. Normally this
% value is set to NVARS. This implies that no
% 'partial pricing' is used.
% 67 Adjust the tuning factor (normally one) to apply
% to the primal and dual error estimates.
% 68 Pass information to the subprogram FULMAT(),
% provided with the SPLP() package, so that a Fortran
% two-dimensional array can be used as the argument
% DATTRV(*).
% 69 Pass an absolute tolerance to use for the feasibility
% test when the usual relative error test indicates
% infeasibility. The nominal value of this tolerance,
% TOLABS, is zero.
%
%
% |---------------|
% |Working Arrays:|
% |---------------|
%
% WORK(*),LW,
% IWORK(*),LIW
% ------------
% The arrays WORK(*) and IWORK(*) are respectively floating point
% and type INTEGER working arrays for SPLP( ) and its
% subprograms. The lengths of these arrays are respectively
% LW and LIW. These parameters must satisfy the inequalities
% noted above under the heading 'Fortran Declarations Required.'
% It is an error if either value is too small.
%
% |----------------------------|
% |Input/Output files required:|
% |----------------------------|
%
% Fortran unit 1 is used by SPLP( ) to store the sparse matrix A
% out of high-speed memory. This direct access file is opened
% within the package under the following two conditions.
% 1. When the Save/Restore feature is used. 2. When the
% constraint matrix is so large that storage out of high-speed
% memory is required. The user may need to close unit 1
% (with deletion from the job step) in the main program unit
% when several calls are made to SPLP( ). A crude
% upper bound for the amount of information written on unit 1
% is 6*nz, where nz is the number of nonzero entries in A.
% The unit number may be redefined to any other positive value
% by means of input in the option array PRGOPT(*).
%
% Fortran unit 2 is used by SPLP( ) only when the Save/Restore
% feature is desired. Normally this feature is not used. It is
% activated by means of input in the option array PRGOPT(*).
% On some computer systems the user may need to open unit
% 2 before executing a call to SPLP( ). This file is type
% sequential and is unformatted.
%
% Fortran unit=I1MACH(2) (check local setting) is used by SPLP( )
% when the printed output feature (KEY=51) is used. Normally
% this feature is not used. It is activated by input in the
% options array PRGOPT(*). For many computer systems I1MACH(2)=6.
%
% |-------|
% |Output:|
% |-------|
%
% INFO,PRIMAL(*),DUALS(*)
% -----------------------
% The integer flag INFO indicates why SPLP( ) has returned to the
% user. If INFO=1 the solution has been computed. In this case
% X(J)=PRIMAL(J) and W(I)=PRIMAL(I+NVARS). The dual variables
% for the equations A*x=w are in the array DUALS(I)=dual for
% equation number I. The dual value for the component X(J) that
% has an upper or lower bound (or both) is returned in
% DUALS(J+MRELAS). The only other values for INFO are .LT. 0.
% The meaning of these values can be found by reading
% the diagnostic message in the output file, or by looking for
% error number = (-INFO) under the heading 'List of SPLP( ) Error
% and Diagnostic Messages.'
% The diagnostic messages are printed using the error processing
% subprogram XERMSG( ) with error category LEVEL=1.
% See the document 'Brief Instr. for Using the Sandia Math.
% subroutine Library,' SAND79-2382, Nov., 1980, for further inform-
% ation about resetting the usual response to a diagnostic message.
%
% BL(*),BU(*),IND(*)
% ------------------
% These arrays are output parameters only under the (unusual)
% circumstances where the stated problem is infeasible, has an
% unbounded optimum value, or both. These respective conditions
% correspond to INFO=-1,-2 or -3. For INFO=-1 or -3 certain comp-
% onents of the vectors x or w will not satisfy the input bounds.
% If component J of X or component I of W does not satisfy its input
% bound because of infeasibility, then IND(J)=-4 or IND(I+NVARS)=-4,
% respectively. For INFO=-2 or -3 certain
% components of the vector x could not be used as basic variables
% because the objective function would have become unbounded.
% In particular if component J of x corresponds to such a variable,
% then IND(J)=-3. Further, if the input value of IND(J)
% =1, then BU(J)=BL(J);
% =2, then BL(J)=BU(J);
% =4, then BL(J)=0.,BU(J)=0.
%
% (The J-th variable in x has been restricted to an appropriate
% feasible value.)
% The negative output value for IND(*) allows the user to identify
% those constraints that are not satisfied or those variables that
% would cause unbounded values of the objective function. Note
% that the absolute value of IND(*), together with BL(*) and BU(*),
% are valid input to SPLP( ). In the case of infeasibility the
% sum of magnitudes of the infeasible values is minimized. Thus
% one could reenter SPLP( ) with these components of x or w now
% fixed at their present values. This involves setting
% the appropriate components of IND(*) = 3, and BL(*) = BU(*).
%
% IBASIS(I),I=1,...,MRELAS
% ------------------------
% This array contains the indices of the variables that are
% in the active basis set at the solution (INFO=1). A value
% of IBASIS(I) between 1 and NVARS corresponds to the variable
% X(IBASIS(I)). A value of IBASIS(I) between NVARS+1 and NVARS+
% MRELAS corresponds to the variable W(IBASIS(I)-NVARS).
%
% Computing with the Matrix A after Calling SPLP( )
% -------------------------------------------------
% Following the return from SPLP( ), nonzero entries of the MRELAS
% by NVARS matrix A are available for usage by the user. The method
% for obtaining the next nonzero in column J with a row index
% strictly greater than I in value, is completed by executing
%
% CALL PNNZRS(I,AIJ,IPLACE,WORK,IWORK,J)
%
% The value of I is also an output parameter. If I.LE.0 on output,
% then there are no more nonzeroes in column J. If I.GT.0, the
% output value for component number I of column J is in AIJ. The
% parameters WORK(*) and IWORK(*) are the same arguments as in the
% call to SPLP( ). The parameter IPLACE is a single INTEGER
% working variable.
%
% The data structure used for storage of the matrix A within SPLP( )
% corresponds to sequential storage by columns as defined in
% SAND78-0785. Note that the names of the subprograms LNNZRS(),
% LCHNGS(),LINITM(),LLOC(),LRWPGE(), and LRWVIR() have been
% changed to PNNZRS(),PCHNGS(),PINITM(),IPLOC(),PRWPGE(), and
% PRWVIR() respectively. The error processing subprogram LERROR()
% is no longer used; XERMSG() is used instead.
%
% |-------------------------------|
% |Subprograms Required by SPLP( )|
% |-------------------------------|
% Called by SPLP() are SPLPMN(),SPLPUP(),SPINIT(),SPOPT(),
% SPLPDM(),SPLPCE(),SPINCW(),SPLPFL(),
% SPLPFE(),SPLPMU().
%
% Error Processing Subprograms XERMSG(),I1MACH(),R1MACH()
%
% Sparse Matrix Subprograms PNNZRS(),PCHNGS(),PRWPGE(),PRWVIR(),
% PINITM(),IPLOC()
%
% Mass Storage File Subprograms SOPENM(),SCLOSM(),SREADP(),SWRITP()
%
% Basic Linear Algebra Subprograms SCOPY(),SASUM(),SDOT()
%
% Sparse Matrix Basis Handling Subprograms LA05AS(),LA05BS(),
% LA05CS(),LA05ED(),MC20AS()
%
% Vector Output Subprograms SVOUT(),IVOUT()
%
% Machine-sensitive Subprograms I1MACH( ),R1MACH( ),
% SOPENM(),SCLOSM(),SREADP(),SWRITP().
% COMMON Block Used
% -----------------
% /LA05DS/ SMALL,LP,LENL,LENU,NCP,LROW,LCOL
% See the document AERE-R8269 for further details.
% |------------------------|
% |Example of SPLP( ) Usage|
% |------------------------|
% program LPEX
% THE OPTIMIZATION PROBLEM IS TO FIND X1, X2, X3 THAT
% MINIMIZE X1 + X2 + X3, X1.GE.0, X2.GE.0, X3 UNCONSTRAINED.
%
% THE UNKNOWNS X1,X2,X3 ARE TO SATISFY CONSTRAINTS
%
% X1 -3*X2 +4*X3 = 5
% X1 -2*X2 .LE.3
% 2*X2 - X3.GE.4
%
% WE FIRST DEFINE THE DEPENDENT VARIABLES
% W1=X1 -3*X2 +4*X3
% W2=X1- 2*X2
% W3= 2*X2 -X3
%
% WE NOW SHOW HOW TO USE SPLP( ) TO SOLVE THIS LINEAR OPTIMIZATION
% PROBLEM. EACH REQUIRED STEP WILL BE SHOWN IN THIS EXAMPLE.
% DIMENSION COSTS(03),PRGOPT(01),DATTRV(18),BL(06),BU(06),IND(06),
% *PRIMAL(06),DUALS(06),IBASIS(06),WORK(079),IWORK(103)
%
% EXTERNAL USRMAT
% MRELAS=3
% NVARS=3
%
% DEFINE THE ARRAY COSTS(*) FOR THE OBJECTIVE FUNCTION.
% COSTS(01)=1.
% COSTS(02)=1.
% COSTS(03)=1.
%
% PLACE THE NONZERO INFORMATION ABOUT THE MATRIX IN DATTRV(*).
% DEFINE COL. 1:
% DATTRV(01)=-1
% DATTRV(02)=1
% DATTRV(03)=1.
% DATTRV(04)=2
% DATTRV(05)=1.
%
% DEFINE COL. 2:
% DATTRV(06)=-2
% DATTRV(07)=1
% DATTRV(08)=-3.
% DATTRV(09)=2
% DATTRV(10)=-2.
% DATTRV(11)=3
% DATTRV(12)=2.
%
% DEFINE COL. 3:
% DATTRV(13)=-3
% DATTRV(14)=1
% DATTRV(15)=4.
% DATTRV(16)=3
% DATTRV(17)=-1.
%
% DATTRV(18)=0
%
% CONSTRAIN X1,X2 TO BE NONNEGATIVE. LET X3 HAVE NO BOUNDS.
% BL(1)=0.
% IND(1)=1
% BL(2)=0.
% IND(2)=1
% IND(3)=4
%
% CONSTRAIN W1=5,W2.LE.3, AND W3.GE.4.
% BL(4)=5.
% BU(4)=5.
% IND(4)=3
% BU(5)=3.
% IND(5)=2
% BL(6)=4.
% IND(6)=1
%
% INDICATE THAT NO MODIFICATIONS TO OPTIONS ARE IN USE.
% PRGOPT(01)=1
%
% DEFINE THE WORKING ARRAY LENGTHS.
% LW=079
% LIW=103
% CALL SPLP(USRMAT,MRELAS,NVARS,COSTS,PRGOPT,DATTRV,
% *BL,BU,IND,INFO,PRIMAL,DUALS,IBASIS,WORK,LW,IWORK,LIW)
%
% CALCULATE VAL, THE MINIMAL VALUE OF THE OBJECTIVE FUNCTION.
% VAL=SDOT(NVARS,COSTS,1,PRIMAL,1)
%
% STOP
% end
% |------------------------|
% |end of Example of Usage |
% |------------------------|
%
% |------------------------------------|
% |Usage of SPLP( ) Subprogram Options.|
% |------------------------------------|
%
% Users frequently have a large variety of requirements for linear
% optimization software. Allowing for these varied requirements
% is at cross purposes with the desire to keep the usage of SPLP( )
% as simple as possible. One solution to this dilemma is as follows.
% (1) Provide a version of SPLP( ) that solves a wide class of
% problems and is easy to use. (2) Identify parameters within SPLP()
% that certain users may want to change. (3) Provide a means
% of changing any selected number of these parameters that does
% not require changing all of them.
%
% Changing selected parameters is done by requiring
% that the user provide an option array, PRGOPT(*), to SPLP( ).
% The contents of PRGOPT(*) inform SPLP( ) of just those options
% that are going to be modified within the total set of possible
% parameters that can be modified. The array PRGOPT(*) is a linked
% list consisting of groups of data of the following form
%
% LINK
% KEY
% SWITCH
% data set
%
% that describe the desired options. The parameters LINK, KEY and
% switch are each one word and are always required. The data set
% can be comprised of several words or can be empty. The number of
% words in the data set for each option depends on the value of
% the parameter KEY.
%
% The value of LINK points to the first entry of the next group
% of data within PRGOPT(*). The exception is when there are no more
% options to change. In that case, LINK=1 and the values for KEY,
% SWITCH and data set are not referenced. The general layout of
% PRGOPT(*) is as follows:
% ...PRGOPT(1)=LINK1 (link to first entry of next group)
% . PRGOPT(2)=KEY1 (KEY to the option change)
% . PRGOPT(3)=SWITCH1 (on/off switch for the option)
% . PRGOPT(4)=data value
% . .
% . .
% . .
% ...PRGOPT(LINK1)=LINK2 (link to first entry of next group)
% . PRGOPT(LINK1+1)=KEY2 (KEY to option change)
% . PRGOPT(LINK1+2)=SWITCH2 (on/off switch for the option)
% . PRGOPT(LINK1+3)=data value
% ... .
% . .
% . .
% ...PRGOPT(LINK)=1 (no more options to change)
%
% A value of LINK that is .LE.0 or .GT. 10000 is an error.
% In this case SPLP( ) returns with an error message, INFO=-14.
% This helps prevent using invalid but positive values of LINK that
% will probably extend beyond the program limits of PRGOPT(*).
% Unrecognized values of KEY are ignored. If the value of SWITCH is
% zero then the option is turned off. For any other value of SWITCH
% the option is turned on. This is used to allow easy changing of
% options without rewriting PRGOPT(*). The order of the options is
% arbitrary and any number of options can be changed with the
% following restriction. To prevent cycling in processing of the
% option array PRGOPT(*), a count of the number of options changed
% is maintained. Whenever this count exceeds 1000 an error message
% (INFO=-15) is printed and the subprogram returns.
%
% In the following description of the options, the value of
% LATP indicates the amount of additional storage that a particular
% option requires. The sum of all of these values (plus one) is
% the minimum dimension for the array PRGOPT(*).
%
% If a user is satisfied with the nominal form of SPLP( ),
% set PRGOPT(1)=1 (or PRGOPT(1)=1.0E0).
%
% Options:
%
% -----KEY = 50. Change from a minimization problem to a maximization
% problem.
% If SWITCH=0 option is off; solve minimization problem.
% =1 option is on; solve maximization problem.
% data set =empty
% LATP=3
%
% -----KEY = 51. Change the amount of printed output. The nominal form
% of SPLP( ) has no printed output.
% The first level of output (SWITCH=1) includes
%
% (1) Minimum dimensions for the arrays COSTS(*),BL(*),BU(*),IND(*),
% PRIMAL(*),DUALS(*),IBASIS(*), and PRGOPT(*).
% (2) Problem dimensions MRELAS,NVARS.
% (3) The types of and values for the bounds on x and w,
% and the values of the components of the vector costs.
% (4) Whether optimization problem is minimization or
% maximization.
% (5) Whether steepest edge or smallest reduced cost criteria used
% for exchanging variables in the revised simplex method.
%
% Whenever a solution has been found, (INFO=1),
%
% (6) the value of the objective function,
% (7) the values of the vectors x and w,
% (8) the dual variables for the constraints A*x=w and the
% bounded components of x,
% (9) the indices of the basic variables,
% (10) the number of revised simplex method iterations,
% (11) the number of full decompositions of the basis matrix.
%
% The second level of output (SWITCH=2) includes all for SWITCH=1
% plus
%
% (12) the iteration number,
% (13) the column number to enter the basis,
% (14) the column number to leave the basis,
% (15) the length of the step taken.
%
% The third level of output (SWITCH=3) includes all for SWITCH=2
% plus
% (16) critical quantities required in the revised simplex method.
% This output is rather voluminous. It is intended to be used
% as a diagnostic tool in case of a failure in SPLP( ).
%
% If SWITCH=0 option is off; no printed output.
% =1 summary output.
% =2 lots of output.
% =3 even more output.
% data set =empty
% LATP=3
%
% -----KEY = 52. Redefine the parameter, IDIGIT, which determines the
% format and precision used for the printed output. In the printed
% output, at least ABS(IDIGIT) decimal digits per number is printed.
% If IDIGIT.LT.0, 72 printing columns are used. IF IDIGIT.GT.0, 133
% printing columns are used.
% If SWITCH=0 option is off; IDIGIT=-4.
% =1 option is on.
% data set =IDIGIT
% LATP=4
%
% -----KEY = 53. Redefine LAMAT and LBM, the lengths of the portions of
% WORK(*) and IWORK(*) that are allocated to the sparse matrix
% storage and the sparse linear equation solver, respectively.
% LAMAT must be .GE. NVARS+7 and LBM must be positive.
% If SWITCH=0 option is off; LAMAT=4*NVARS+7
% LBM =8*MRELAS.
% =1 option is on.
% data set =LAMAT
% LBM
% LATP=5
%
% -----KEY = 54. Redefine IPAGEF, the file number where the pages of the
% sparse data matrix are stored. IPAGEF must be positive and
% different from ISAVE (see option 56).
% If SWITCH=0 option is off; IPAGEF=1.
% =1 option is on.
% data set =IPAGEF
% LATP=4
%
% -----KEY = 55. Partial results have been computed and stored on unit
% number ISAVE (see option 56), during a previous run of
% SPLP( ). This is a continuation from these partial results.
% The arrays COSTS(*),BL(*),BU(*),IND(*) do not have to have
% the same values as they did when the checkpointing occurred.
% This feature makes it possible for the user to do certain
% types of parameter studies such as changing costs and varying
% the constraints of the problem. This file is rewound both be-
% fore and after reading the partial results.
% If SWITCH=0 option is off; start a new problem.
% =1 option is on; continue from partial results
% that are stored in file ISAVE.
% data set = empty
% LATP=3
%
% -----KEY = 56. Redefine ISAVE, the file number where the partial
% results are stored (see option 57). ISAVE must be positive and
% different from IPAGEF (see option 54).
% If SWITCH=0 option is off; ISAVE=2.
% =1 option is on.
% data set =ISAVE
% LATP=4
%
% -----KEY = 57. Save the partial results after maximum number of
% iterations, MAXITR, or at the optimum. When this option is on,
% data essential to continuing the calculation is saved on a file
% using a Fortran binary write operation. The data saved includes
% all the information about the sparse data matrix A. Also saved
% is information about the current basis. Nominally the partial
% results are saved on Fortran unit 2. This unit number can be
% redefined (see option 56). If the savemlv option is on,
% this file must be opened (or declared) by the user prior to the
% call to SPLP( ). A crude upper bound for the number of words
% written to this file is 6*nz. Here nz= number of nonzeros in A.
% If SWITCH=0 option is off; do not savemlv partial results.
% =1 option is on; savemlv partial results.
% data set = empty
% LATP=3
%
% -----KEY = 58. Redefine the maximum number of iterations, MAXITR, to
% be taken before returning to the user.
% If SWITCH=0 option is off; MAXITR=3*(NVARS+MRELAS).
% =1 option is on.
% data set =MAXITR
% LATP=4
%
% -----KEY = 59. Provide SPLP( ) with exactly MRELAS indices which
% comprise a feasible, nonsingular basis. The basis must define a
% feasible point: values for x and w such that A*x=w and all the
% stated bounds on x and w are satisfied. The basis must also be
% nonsingular. The failure of either condition will cause an error
% message (INFO=-23 or =-24, respectively). Normally, SPLP( ) uses
% identity matrix columns which correspond to the components of w.
% This option would normally not be used when restarting from
% a previously saved run (KEY=57).
% In numbering the unknowns,
% the components of x are numbered (1-NVARS) and the components
% of w are numbered (NVARS+1)-(NVARS+MRELAS). A value for an
% index .LE. 0 or .GT. (NVARS+MRELAS) is an error (INFO=-16).
% If SWITCH=0 option is off; SPLP( ) chooses the initial basis.
% =1 option is on; user provides the initial basis.
% data set =MRELAS indices of basis; order is arbitrary.
% LATP=MRELAS+3
%
% -----KEY = 60. Provide the scale factors for the columns of the data
% matrix A. Normally, SPLP( ) computes the scale factors as the
% reciprocals of the max. norm of each column.
% If SWITCH=0 option is off; SPLP( ) computes the scale factors.
% =1 option is on; user provides the scale factors.
% data set =scaling for column J, J=1,NVARS; order is sequential.
% LATP=NVARS+3
%
% -----KEY = 61. Provide a scale factor, COSTSC, for the vector of
% costs. Normally, SPLP( ) computes this scale factor to be the
% reciprocal of the max. norm of the vector costs after the column
% scaling has been applied.
% If SWITCH=0 option is off; SPLP( ) computes COSTSC.
% =1 option is on; user provides COSTSC.
% data set =COSTSC
% LATP=4
%
% -----KEY = 62. Provide size parameters, ASMALL and ABIG, the smallest
% and largest magnitudes of nonzero entries in the data matrix A,
% respectively. When this option is on, SPLP( ) will check the
% nonzero entries of A to see if they are in the range of ASMALL and
% ABIG. If an entry of A is not within this range, SPLP( ) returns
% an error message, INFO=-22. Both ASMALL and ABIG must be positive
% with ASMALL .LE. ABIG. Otherwise, an error message is returned,
% INFO=-17.
% If SWITCH=0 option is off; no checking of the data matrix is done
% =1 option is on; checking is done.
% data set =ASMALL
% ABIG
% LATP=5
%
% -----KEY = 63. Redefine the relative tolerance, TOLLS, used in
% checking if the residuals are feasible. Normally,
% TOLLS=RELPR, where RELPR is the machine precision.
% If SWITCH=0 option is off; TOLLS=RELPR.
% =1 option is on.
% data set =TOLLS
% LATP=4
%
% -----KEY = 64. Use the minimum reduced cost pricing strategy to choose
% columns to enter the basis. Normally, SPLP( ) uses the steepest
% edge pricing strategy which is the best local move. The steepest
% edge pricing strategy generally uses fewer iterations than the
% minimum reduced cost pricing, but each iteration costs more in the
% number of calculations done. The steepest edge pricing is
% considered to be more efficient. However, this is very problem
% dependent. That is why SPLP( ) provides the option of either
% pricing strategy.
% If SWITCH=0 option is off; steepest option edge pricing is used.
% =1 option is on; minimum reduced cost pricing is used.
% data set =empty
% LATP=3
%
% -----KEY = 65. Redefine MXITBR, the number of iterations between
% recalculating the error in the primal solution. Normally, MXITBR
% is set to 10. The error in the primal solution is used to monitor
% the error in solving the linear system. This is an expensive
% calculation and every tenth iteration is generally often enough.
% If SWITCH=0 option is off; MXITBR=10.
% =1 option is on.
% data set =MXITBR
% LATP=4
%
% -----KEY = 66. Redefine NPP, the number of negative reduced costs
% (at most) to be found at each iteration of choosing
% a variable to enter the basis. Normally NPP is set
% to NVARS which implies that all of the reduced costs
% are computed at each such step. This 'partial
% pricing' may very well increase the total number
% of iterations required. However it decreases the
% number of calculations at each iteration.
% therefore the effect on overall efficiency is quite
% problem-dependent.
%
% if SWITCH=0 option is off; NPP=NVARS
% =1 option is on.
% data set =NPP
% LATP=4
%
% -----KEY = 67. Redefine the tuning factor (PHI) used to scale the
% error estimates for the primal and dual linear algebraic systems
% of equations. Normally, PHI = 1.0E0, but in some environments it
% may be necessary to reset PHI to the range 0.001-0.01. This is
% particularly important for machines with short word lengths.
%
% if SWITCH = 0 option is off; PHI=1.0E0.
% = 1 option is on.
% Data Set = PHI
% LATP=4
%
% -----KEY = 68. Used together with the subprogram FULMAT(), provided
% with the SPLP() package, for passing a standard Fortran two-
% dimensional array containing the constraint matrix. Thus the sub-
% program FULMAT must be declared in a Fortran EXTERNAL statement.
% The two-dimensional array is passed as the argument DATTRV.
% The information about the array and problem dimensions are passed
% in the option array PRGOPT(*). It is an error if FULMAT() is
% used and this information is not passed in PRGOPT(*).
%
% if SWITCH = 0 option is off; this is an error is FULMAT() is
% used.
% = 1 option is on.
% Data Set = IA = row dimension of two-dimensional array.
% MRELAS = number of constraint equations.
% NVARS = number of dependent variables.
% LATP = 6
% -----KEY = 69. Normally a relative tolerance (TOLLS, see option 63)
% is used to decide if the problem is feasible. If this test fails
% an absolute test will be applied using the value TOLABS.
% Nominally TOLABS = zero.
% If SWITCH = 0 option is off; TOLABS = zero.
% = 1 option is on.
% Data set = TOLABS
% LATP = 4
%
% |-----------------------------|
% |Example of Option array Usage|
% |-----------------------------|
% To illustrate the usage of the option array, let us suppose that
% the user has the following nonstandard requirements:
%
% a) Wants to change from minimization to maximization problem.
% b) Wants to limit the number of simplex steps to 100.
% c) Wants to savemlv the partial results after 100 steps on
% Fortran unit 2.
%
% After these 100 steps are completed the user wants to continue the
% problem (until completed) using the partial results saved on
% Fortran unit 2. Here are the entries of the array PRGOPT(*)
% that accomplish these tasks. (The definitions of the other
% required input parameters are not shown.)
%
% CHANGE TO A MAXIMIZATION PROBLEM; KEY=50.
% PRGOPT(01)=4
% PRGOPT(02)=50
% PRGOPT(03)=1
%
% LIMIT THE NUMBER OF SIMPLEX STEPS TO 100; KEY=58.
% PRGOPT(04)=8
% PRGOPT(05)=58
% PRGOPT(06)=1
% PRGOPT(07)=100
%
% SAVE THE PARTIAL RESULTS, AFTER 100 STEPS, ON FORTRAN
% UNIT 2; KEY=57.
% PRGOPT(08)=11
% PRGOPT(09)=57
% PRGOPT(10)=1
%
% NO MORE OPTIONS TO CHANGE.
% PRGOPT(11)=1
% The user makes the CALL statement for SPLP( ) at this point.
% Now to restart, using the partial results after 100 steps, define
% new values for the array PRGOPT(*):
%
% AGAIN INFORM SPLP( ) THAT THIS IS A MAXIMIZATION PROBLEM.
% PRGOPT(01)=4
% PRGOPT(02)=50
% PRGOPT(03)=1
%
% RESTART, USING SAVED PARTIAL RESULTS; KEY=55.
% PRGOPT(04)=7
% PRGOPT(05)=55
% PRGOPT(06)=1
%
% NO MORE OPTIONS TO CHANGE. THE SUBPROGRAM SPLP( ) IS NO LONGER
% LIMITED TO 100 SIMPLEX STEPS BUT WILL RUN UNTIL COMPLETION OR
% MAX.=3*(MRELAS+NVARS) ITERATIONS.
% PRGOPT(07)=1
% The user now makes a CALL to subprogram SPLP( ) to compute the
% solution.
% |-------------------------------------------|
% |end of Usage of SPLP( ) Subprogram Options.|
% |-------------------------------------------|
%
% |----------------------------------------------|
% |List of SPLP( ) Error and Diagnostic Messages.|
% |----------------------------------------------|
% This section may be required to understand the meanings of the
% error flag =-INFO that may be returned from SPLP( ).
%
% -----1. There is no set of values for x and w that satisfy A*x=w and
% the stated bounds. The problem can be made feasible by ident-
% ifying components of w that are now infeasible and then rede-
% signating them as free variables. Subprogram SPLP( ) only
% identifies an infeasible problem; it takes no other action to
% change this condition. Message:
% SPLP( ). THE PROBLEM APPEARS TO BE INFEASIBLE.
% ERROR NUMBER = 1
%
% 2. One of the variables in either the vector x or w was con-
% strained at a bound. Otherwise the objective function value,
% (transpose of costs)*x, would not have a finite optimum.
% Message:
% SPLP( ). THE PROBLEM APPEARS TO HAVE NO FINITE SOLN.
% ERROR NUMBER = 2
%
% 3. Both of the conditions of 1. and 2. above have occurred.
% Message:
% SPLP( ). THE PROBLEM APPEARS TO BE INFEASIBLE AND TO
% HAVE NO FINITE SOLN.
% ERROR NUMBER = 3
%
% -----4. The REAL and INTEGER working arrays, WORK(*) and IWORK(*),
% are not long enough. The values (I1) and (I2) in the message
% below will give you the minimum length required. Also redefine
% LW and LIW, the lengths of these arrays. Message:
% SPLP( ). WORK OR IWORK IS NOT LONG ENOUGH. LW MUST BE (I1)
% AND LIW MUST BE (I2).
% IN ABOVE MESSAGE, I1= 0
% IN ABOVE MESSAGE, I2= 0
% ERROR NUMBER = 4
%
% -----5. and 6. These error messages often mean that one or more
% arguments were left out of the call statement to SPLP( ) or
% that the values of MRELAS and NVARS have been over-written
% by garbage. Messages:
% SPLP( ). VALUE OF MRELAS MUST BE .GT.0. NOW=(I1).
% IN ABOVE MESSAGE, I1= 0
% ERROR NUMBER = 5
%
% SPLP( ). VALUE OF NVARS MUST BE .GT.0. NOW=(I1).
% IN ABOVE MESSAGE, I1= 0
% ERROR NUMBER = 6
%
% -----7.,8., and 9. These error messages can occur as the data matrix
% is being defined by either USRMAT( ) or the user-supplied sub-
% program, 'NAME'( ). They would indicate a mistake in the contents
% of DATTRV(*), the user-written subprogram or that data has been
% over-written.
% Messages:
% SPLP( ). MORE THAN 2*NVARS*MRELAS ITERS. DEFINING OR UPDATING
% MATRIX DATA.
% ERROR NUMBER = 7
%
% SPLP( ). ROW INDEX (I1) OR COLUMN INDEX (I2) IS OUT OF RANGE.
% IN ABOVE MESSAGE, I1= 1
% IN ABOVE MESSAGE, I2= 12
% ERROR NUMBER = 8
%
% SPLP( ). INDICATION FLAG (I1) FOR MATRIX DATA MUST BE
% EITHER 0 OR 1.
% IN ABOVE MESSAGE, I1= 12
% ERROR NUMBER = 9
%
% -----10. and 11. The type of bound (even no bound) and the bounds
% must be specified for each independent variable. If an independent
% variable has both an upper and lower bound, the bounds must be
% consistent. The lower bound must be .LE. the upper bound.
% Messages:
% SPLP( ). INDEPENDENT VARIABLE (I1) IS NOT DEFINED.
% IN ABOVE MESSAGE, I1= 1
% ERROR NUMBER = 10
%
% SPLP( ). LOWER BOUND (R1) AND UPPER BOUND (R2) FOR INDEP.
% VARIABLE (I1) ARE NOT CONSISTENT.
% IN ABOVE MESSAGE, I1= 1
% IN ABOVE MESSAGE, R1= 0.
% IN ABOVE MESSAGE, R2= -.1000000000E+01
% ERROR NUMBER = 11
%
% -----12. and 13. The type of bound (even no bound) and the bounds
% must be specified for each dependent variable. If a dependent
% variable has both an upper and lower bound, the bounds must be
% consistent. The lower bound must be .LE. the upper bound.
% Messages:
% SPLP( ). DEPENDENT VARIABLE (I1) IS NOT DEFINED.
% IN ABOVE MESSAGE, I1= 1
% ERROR NUMBER = 12
%
% SPLP( ). LOWER BOUND (R1) AND UPPER BOUND (R2) FOR DEP.
% VARIABLE (I1) ARE NOT CONSISTENT.
% IN ABOVE MESSAGE, I1= 1
% IN ABOVE MESSAGE, R1= 0.
% IN ABOVE MESSAGE, R2= -.1000000000E+01
% ERROR NUMBER = 13
%
% -----14. - 21. These error messages can occur when processing the
% option array, PRGOPT(*), supplied by the user. They would
% indicate a mistake in defining PRGOPT(*) or that data has been
% over-written. See heading Usage of SPLP( )
% Subprogram Options, for details on how to define PRGOPT(*).
% Messages:
% SPLP( ). THE USER OPTION ARRAY HAS UNDEFINED DATA.
% ERROR NUMBER = 14
%
% SPLP( ). OPTION ARRAY PROCESSING IS CYCLING.
% ERROR NUMBER = 15
%
% SPLP( ). AN INDEX OF USER-SUPPLIED BASIS IS OUT OF RANGE.
% ERROR NUMBER = 16
%
% SPLP( ). SIZE PARAMETERS FOR MATRIX MUST BE SMALLEST AND LARGEST
% MAGNITUDES OF NONZERO ENTRIES.
% ERROR NUMBER = 17
%
% SPLP( ). THE NUMBER OF REVISED SIMPLEX STEPS BETWEEN CHECK-POINTS
% MUST BE POSITIVE.
% ERROR NUMBER = 18
%
% SPLP( ). FILE NUMBERS FOR SAVED DATA AND MATRIX PAGES MUST BE
% POSITIVE AND NOT EQUAL.
% ERROR NUMBER = 19
%
% SPLP( ). USER-DEFINED VALUE OF LAMAT (I1)
% MUST BE .GE. NVARS+7.
% IN ABOVE MESSAGE, I1= 1
% ERROR NUMBER = 20
%
% SPLP( ). USER-DEFINED VALUE OF LBM MUST BE .GE. 0.
% ERROR NUMBER = 21
%
% -----22. The user-option, number 62, to check the size of the matrix
% data has been used. An element of the matrix does not lie within
% the range of ASMALL and ABIG, parameters provided by the user.
% (See the heading: Usage of SPLP( ) Subprogram Options,
% for details about this feature.) Message:
% SPLP( ). A MATRIX ELEMENT'S SIZE IS OUT OF THE SPECIFIED RANGE.
% ERROR NUMBER = 22
%
% -----23. The user has provided an initial basis that is singular.
% In this case, the user can remedy this problem by letting
% subprogram SPLP( ) choose its own initial basis. Message:
% SPLP( ). A SINGULAR INITIAL BASIS WAS ENCOUNTERED.
% ERROR NUMBER = 23
%
% -----24. The user has provided an initial basis which is infeasible.
% The x and w values it defines do not satisfy A*x=w and the stated
% bounds. In this case, the user can let subprogram SPLP( )
% choose its own initial basis. Message:
% SPLP( ). AN INFEASIBLE INITIAL BASIS WAS ENCOUNTERED.
% ERROR NUMBER = 24
%
% -----25. Subprogram SPLP( ) has completed the maximum specified number
% of iterations. (The nominal maximum number is 3*(MRELAS+NVARS).)
% The results, necessary to continue on from
% this point, can be saved on Fortran unit 2 by activating option
% KEY=57. If the user anticipates continuing the calculation, then
% the contents of Fortran unit 2 must be retained intact. This
% is not done by subprogram SPLP( ), so the user needs to savemlv unit
% 2 by using the appropriate system commands. Message:
% SPLP( ). MAX. ITERS. (I1) TAKEN. UP-TO-DATE RESULTS
% SAVED ON FILE (I2). IF(I2)=0, NO SAVE.
% IN ABOVE MESSAGE, I1= 500
% IN ABOVE MESSAGE, I2= 2
% ERROR NUMBER = 25
%
% -----26. This error should never happen. Message:
% SPLP( ). MOVED TO A SINGULAR POINT. THIS SHOULD NOT HAPPEN.
% ERROR NUMBER = 26
%
% -----27. The subprogram LA05A( ), which decomposes the basis matrix,
% has returned with an error flag (R1). (See the document,
% 'Fortran subprograms for handling sparse linear programming
% bases', AERE-R8269, J.K. Reid, Jan., 1976, H.M. Stationery Office,
% for an explanation of this error.) Message:
% SPLP( ). LA05A( ) RETURNED ERROR FLAG (R1) BELOW.
% IN ABOVE MESSAGE, R1= -.5000000000E+01
% ERROR NUMBER = 27
%
% -----28. The sparse linear solver package, LA05*( ), requires more
% space. The value of LBM must be increased. See the companion
% document, Usage of SPLP( ) Subprogram Options, for details on how
% to increase the value of LBM. Message:
% SPLP( ). SHORT ON STORAGE FOR LA05*( ) PACKAGE. USE PRGOPT(*)
% TO GIVE MORE.
% ERROR NUMBER = 28
%
% -----29. The row dimension of the two-dimensional Fortran array,
% the number of constraint equations (MRELAS), and the number
% of variables (NVARS), were not passed to the subprogram
% FULMAT(). See KEY = 68 for details. Message:
% FULMAT() OF SPLP() PACKAGE. ROW DIM., MRELAS, NVARS ARE
% MISSING FROM PRGOPT(*).
% ERROR NUMBER = 29
%
% |------------------------------------------------------|
% |end of List of SPLP( ) Error and Diagnostic Messages. |
% |------------------------------------------------------|
%***REFERENCES R. J. Hanson and K. L. Hiebert, A sparse linear
% programming subprogram, Report SAND81-0297, Sandia
% National Laboratories, 1981.
%***ROUTINES CALLED SPLPMN, XERMSG
%***REVISION HISTORY (YYMMDD)
% 811215 DATE WRITTEN
% 890605 Corrected references to XERRWV. (WRB)
% 890605 Removed unreferenced labels. (WRB)
% 890605 REVISION DATE from Version 3.2
% 891214 Prologue converted to Version 4.0 format. (BAB)
% 900315 CALLs to XERROR changed to CALLs to XERMSG. (THJ)
% 900510 Convert XERRWV calls to XERMSG calls. (RWC)
% 920501 Reformatted the REFERENCES section. (WRB)
%***end PROLOGUE SPLP
bl_shape=size(bl);bl=reshape(bl,1,[]);
bu_shape=size(bu);bu=reshape(bu,1,[]);
costs_shape=size(costs);costs=reshape(costs,1,[]);
dattrv_shape=size(dattrv);dattrv=reshape(dattrv,1,[]);
duals_shape=size(duals);duals=reshape(duals,1,[]);
prgopt_shape=size(prgopt);prgopt=reshape(prgopt,1,[]);
primal_shape=size(primal);primal=reshape(primal,1,[]);
work_shape=size(work);work=reshape(work,1,[]);
if isempty(zero), zero=0; end;
%
ibasis_shape=size(ibasis);ibasis=reshape(ibasis,1,[]);
ind_shape=size(ind);ind=reshape(ind,1,[]);
iwork_shape=size(iwork);iwork=reshape(iwork,1,[]);
if isempty(xern1), xern1=repmat(' ',1,8); end;
if isempty(xern2), xern2=repmat(' ',1,8); end;
%
%
%***FIRST EXECUTABLE STATEMENT SPLP
zero = 0.0e0;
iopt = 1;
%
% VERIFY THAT MRELAS, NVARS .GT. 0.
%
if( mrelas<=0 )
xern1=sprintf(['%8i'], mrelas);
xermsg('SLATEC','SPLP',['VALUE OF MRELAS MUST BE ',['.GT. 0. NOW = ',xern1]],5,1);
info = -5;
%
elseif( nvars<=0 ) ;
xern1=sprintf(['%8i'], nvars);
xermsg('SLATEC','SPLP',['VALUE OF NVARS MUST BE ',['.GT. 0. NOW = ',xern1]],6,1);
info = -6;
else;
%
lmx = fix(4.*nvars + 7);
lbm = fix(8.*mrelas);
last = 1;
iadbig = 10000;
ictmax = 1000;
ictopt = 0;
%
% LOOK IN OPTION ARRAY FOR CHANGES TO WORK ARRAY LENGTHS.
while( true );
next = fix(prgopt(last));
if( next<=0 || next>iadbig )
%
% THE CHECKS FOR SMALL OR LARGE VALUES OF NEXT ARE TO PREVENT
% WORKING WITH UNDEFINED DATA.
nerr = 14;
[dumvar1,dumvar2,dumvar3,nerr,iopt]=xermsg('SLATEC','SPLP','THE USER OPTION ARRAY HAS UNDEFINED DATA.',nerr,iopt);
info = fix(-nerr);
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
return;
elseif( next==1 ) ;
%
% CHECK LENGTH VALIDITY OF SPARSE MATRIX STAGING AREA.
%
if( lmx<nvars+7 )
xern1=sprintf(['%8i'], lmx);
xermsg('SLATEC','SPLP',['USER-DEFINED VALUE OF ',['LAMAT = ',[xern1,' MUST BE .GE. NVARS+7.']]],20,1);
info = -20;
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
return;
%
% TRIVIAL CHECK ON LENGTH OF LA05*() MATRIX AREA.
elseif( lbm>=0 ) ;
%
% DEFINE POINTERS FOR STARTS OF SUBARRAYS USED IN WORK(*)
% AND IWORK(*) IN OTHER SUBPROGRAMS OF THE PACKAGE.
lamat = 1;
lcsc = fix(lamat + lmx);
lcolnr = fix(lcsc + nvars);
lerd = fix(lcolnr + nvars);
lerp = fix(lerd + mrelas);
lbasma = fix(lerp + mrelas);
lwr = fix(lbasma + lbm);
lrz = fix(lwr + mrelas);
lrg = fix(lrz + nvars + mrelas);
lrprim = fix(lrg + nvars + mrelas);
lrhs = fix(lrprim + mrelas);
lww = fix(lrhs + mrelas);
lwork = fix(lww + mrelas - 1);
limat = 1;
libb = fix(limat + lmx);
librc = fix(libb + nvars + mrelas);
lipr = fix(librc + 2.*lbm);
liwr = fix(lipr + 2.*mrelas);
liwork = fix(liwr + 8.*mrelas - 1);
%
% CHECK ARRAY LENGTH VALIDITY OF WORK(*), IWORK(*).
%
if( lw<lwork || liw<liwork )
xern1=sprintf(['%8i'], lwork);
xern2=sprintf(['%8i'], liwork);
xermsg('SLATEC','SPLP',['WORK OR IWORK IS NOT LONG ',['ENOUGH. LW MUST BE = ',[xern1,[' AND LIW MUST BE = ',xern2]]]],4,1);
info = -4;
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
return;
else;
%
[usrmat,mrelas,nvars,costs,prgopt,dattrv,bl,bu,ind,info,primal,duals,dumvar13,dumvar14,dumvar15,dumvar16,dumvar17,dumvar18,dumvar19,dumvar20,dumvar21,dumvar22,dumvar23,dumvar24,lmx,lbm,ibasis,dumvar28,dumvar29,dumvar30,dumvar31,dumvar32]=splpmn(usrmat,mrelas,nvars,costs,prgopt,dattrv,bl,bu,ind,info,primal,duals,work(sub2ind(size(work),max(lamat,1)):end),work(sub2ind(size(work),max(lcsc,1)):end),work(sub2ind(size(work),max(lcolnr,1)):end),work(sub2ind(size(work),max(lerd,1)):end),work(sub2ind(size(work),max(lerp,1)):end),work(sub2ind(size(work),max(lbasma,1)):end),work(sub2ind(size(work),max(lwr,1)):end),work(sub2ind(size(work),max(lrz,1)):end),work(sub2ind(size(work),max(lrg,1)):end),work(sub2ind(size(work),max(lrprim,1)):end),work(sub2ind(size(work),max(lrhs,1)):end),work(sub2ind(size(work),max(lww,1)):end),lmx,lbm,ibasis,iwork(sub2ind(size(iwork),max(libb,1)):end),iwork(sub2ind(size(iwork),max(limat,1)):end),iwork(librc:end),iwork(sub2ind(size(iwork),max(lipr,1)):end),iwork(sub2ind(size(iwork),max(liwr,1)):end)); dumvar13i=find((work(sub2ind(size(work),max(lamat,1)):end))~=(dumvar13));dumvar14i=find((work(sub2ind(size(work),max(lcsc,1)):end))~=(dumvar14));dumvar15i=find((work(sub2ind(size(work),max(lcolnr,1)):end))~=(dumvar15));dumvar16i=find((work(sub2ind(size(work),max(lerd,1)):end))~=(dumvar16));dumvar17i=find((work(sub2ind(size(work),max(lerp,1)):end))~=(dumvar17));dumvar18i=find((work(sub2ind(size(work),max(lbasma,1)):end))~=(dumvar18));dumvar19i=find((work(sub2ind(size(work),max(lwr,1)):end))~=(dumvar19));dumvar20i=find((work(sub2ind(size(work),max(lrz,1)):end))~=(dumvar20));dumvar21i=find((work(sub2ind(size(work),max(lrg,1)):end))~=(dumvar21));dumvar22i=find((work(sub2ind(size(work),max(lrprim,1)):end))~=(dumvar22));dumvar23i=find((work(sub2ind(size(work),max(lrhs,1)):end))~=(dumvar23));dumvar24i=find((work(sub2ind(size(work),max(lww,1)):end))~=(dumvar24));dumvar28i=find((iwork(sub2ind(size(iwork),max(libb,1)):end))~=(dumvar28));dumvar29i=find((iwork(sub2ind(size(iwork),max(limat,1)):end))~=(dumvar29));dumvar30i=find((iwork(librc:end))~=(dumvar30));dumvar31i=find((iwork(sub2ind(size(iwork),max(lipr,1)):end))~=(dumvar31));dumvar32i=find((iwork(sub2ind(size(iwork),max(liwr,1)):end))~=(dumvar32)); work(lamat-1+dumvar13i)=dumvar13(dumvar13i); work(lcsc-1+dumvar14i)=dumvar14(dumvar14i); work(lcolnr-1+dumvar15i)=dumvar15(dumvar15i); work(lerd-1+dumvar16i)=dumvar16(dumvar16i); work(lerp-1+dumvar17i)=dumvar17(dumvar17i); work(lbasma-1+dumvar18i)=dumvar18(dumvar18i); work(lwr-1+dumvar19i)=dumvar19(dumvar19i); work(lrz-1+dumvar20i)=dumvar20(dumvar20i); work(lrg-1+dumvar21i)=dumvar21(dumvar21i); work(lrprim-1+dumvar22i)=dumvar22(dumvar22i); work(lrhs-1+dumvar23i)=dumvar23(dumvar23i); work(lww-1+dumvar24i)=dumvar24(dumvar24i); iwork(libb-1+dumvar28i)=dumvar28(dumvar28i); iwork(limat-1+dumvar29i)=dumvar29(dumvar29i); iwork(librc-1+dumvar30i)=dumvar30(dumvar30i); iwork(lipr-1+dumvar31i)=dumvar31(dumvar31i); iwork(liwr-1+dumvar32i)=dumvar32(dumvar32i);
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
return;
end;
else;
nerr = 21;
[dumvar1,dumvar2,dumvar3,nerr,iopt]=xermsg('SLATEC','SPLP','USER-DEFINED VALUE OF LBM MUST BE .GE. 0.',nerr,iopt);
info = fix(-nerr);
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
return;
end;
else;
if( ictopt>ictmax )
break;
end;
key = fix(prgopt(last+1));
%
% IF KEY = 53, USER MAY SPECIFY LENGTHS OF PORTIONS
% OF WORK(*) AND IWORK(*) THAT ARE ALLOCATED TO THE
% SPARSE MATRIX STORAGE AND SPARSE LINEAR EQUATION
% SOLVING.
if( key==53 )
if( prgopt(last+2)~=zero )
lmx = fix(prgopt(last+3));
lbm = fix(prgopt(last+4));
end;
end;
ictopt = fix(ictopt + 1);
last = fix(next);
end;
end;
nerr = 15;
[dumvar1,dumvar2,dumvar3,nerr,iopt]=xermsg('SLATEC','SPLP','OPTION ARRAY PROCESSING IS CYCLING.',nerr,iopt);
info = fix(-nerr);
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
return;
end;
%
% CALL SPLPMN(USRMAT,MRELAS,NVARS,COSTS,PRGOPT,DATTRV,
% 1 BL,BU,IND,INFO,PRIMAL,DUALS,AMAT,
% 2 CSC,COLNRM,ERD,ERP,BASMAT,
% 3 WR,RZ,RG,RPRIM,RHS,
% 4 WW,LMX,LBM,IBASIS,IBB,IMAT,
% 5 IBRC,IPR,IWR)
%
bl_shape=zeros(bl_shape);bl_shape(:)=bl(1:numel(bl_shape));bl=bl_shape;
bu_shape=zeros(bu_shape);bu_shape(:)=bu(1:numel(bu_shape));bu=bu_shape;
costs_shape=zeros(costs_shape);costs_shape(:)=costs(1:numel(costs_shape));costs=costs_shape;
dattrv_shape=zeros(dattrv_shape);dattrv_shape(:)=dattrv(1:numel(dattrv_shape));dattrv=dattrv_shape;
duals_shape=zeros(duals_shape);duals_shape(:)=duals(1:numel(duals_shape));duals=duals_shape;
prgopt_shape=zeros(prgopt_shape);prgopt_shape(:)=prgopt(1:numel(prgopt_shape));prgopt=prgopt_shape;
primal_shape=zeros(primal_shape);primal_shape(:)=primal(1:numel(primal_shape));primal=primal_shape;
work_shape=zeros(work_shape);work_shape(:)=work(1:numel(work_shape));work=work_shape;
ibasis_shape=zeros(ibasis_shape);ibasis_shape(:)=ibasis(1:numel(ibasis_shape));ibasis=ibasis_shape;
ind_shape=zeros(ind_shape);ind_shape(:)=ind(1:numel(ind_shape));ind=ind_shape;
iwork_shape=zeros(iwork_shape);iwork_shape(:)=iwork(1:numel(iwork_shape));iwork=iwork_shape;
end %subroutine splp
%DECK SPLPFE
|
|
