TYPE Statement
- TYPE variable ;
The TYPE statement specifies a character variable in the problem
data set that contains the type identifier for each observation.
This variable has keyword values that specify how the LP procedure
should interpret the observation. If the TYPE statement is omitted,
the procedure assumes that the variable named _TYPE_
contains the type keywords.
For the dense input format, the type variable identifies
the constraint and objective rows and rows that contain
information about the variables. The type variable should have
nonmissing values in all observations.
For the sparse input format,
the type variable identifies a model's rows and columns.
In an observation, a nonmissing type
is associated with either a row or a column.
If there are many columns sharing the same type, you can define
a row of that type. Then, any nonmissing values
in that row set the types of the corresponding columns.
The following
are valid values for the TYPE variable in an observation:
- MIN
- contains the price coefficients of an objective row, for example,
c in the problem (mip), to be minimized.
- MAX
- contains the price coefficients
of an objective row, for example, c,
to be maximized.
- EQ (=)
- contains coefficients of an equality constrained row.
- LE ()
- contains coefficients of an inequality, less than or equal to,
constrained row.
- GE ()
- contains coefficients of an inequality, greater than or equal to,
constrained row.
- SOSEQ
- identifies the row as specifying a special ordered set.
The variables flagged in this row are members of a set
exactly one of
which must be above its lower bound in the optimal solution.
Note that variables in this type of special ordered set must
be integer.
- SOSLE
- identifies the row as specifying a special ordered set.
The variables flagged in this row are members of a set in
which only one can be above its lower bound in the optimal solution.
- UNRSTRCT
- identifies
those structural variables
to be considered as unrestricted variables.
These are variables for which
li=-INFINITY and ui=+INFINITY.
Any variable that has a 1 in this observation is considered an
unrestricted variable.
- LOWERBD
- identifies lower bounds on the structural
variables. If all structural variables are to be nonnegative,
that is, li = 0, then you do not need to include an observation
with the LOWERBD keyword in a variable specified in the TYPE statement.
Missing values for variables in a lower-bound row
indicate that the variable has zero lower bound.
Note: A variable with lower or upper bounds cannot be
identified as unrestricted.
- UPPERBD
- identifies upper bounds ui on the structural variables.
For each structural variable that is to have an upper bound
ui=+INFINITY, the observation must contain
a missing value or the current value of INFINITY. All other values are
interpreted as upper bounds, including 0.
- FIXED
- identifies variables that have fixed values.
A nonmissing value in a row with FIXED type keyword
gives the constant value of that variable.
- INTEGER
- identifies variables that are integer constrained.
In a feasible solution, these variables must
have integer values. A missing value
in a row with INTEGER type
keyword indicates that that variable is not integer constrained.
The value of variables in the INTEGER row gives an ordering to
the integer-constrained variables
that is used when the VARSELECT= option equals PRIOR.
Note: Every integer-constrained variable must have an upper bound
defined in a row with type UPPERBD.
See
the Controlling the Branch and Bound Search
for further discussion.
- BINARY
- identifies variables that are constrained to be either 0 or 1.
This is equivalent to specifying that the variable is an integer
variable and has a lower bound of 0 and an upper bound of 1.
A missing value
in a row with BINARY type
keyword indicates that that variable is not constrained to be 0 or 1.
The value of variables in the BINARY row gives an ordering to
the integer-constrained variables
that is used when the VARSELECT= option equals PRIOR.
See
the Controlling the Branch and Bound Search
for further discussion.
- BASIC
- identifies variables that form
an initial basic feasible solution.
A missing value in a row with BASIC type indicates
that that variable is not basic.
- PRICESEN
- identifies a vector that is used to evaluate the sensitivity of
the optimal solution to changes in the objective function.
See
the Price Sensitivity Analysis and
Price Parametric programming
for further discussion.
- FREE
- identifies a nonbinding
constraint. Any number of FREE constraints can appear in a problem
data set.
- RHS
- identifies a right-hand-side column in the sparse input format.
This will replace the RHS statement. It is useful when converting
the MPS format into the sparse format of PROC LP.
See
the Converting MPS Format
for more information.
- RHSSEN
- identifies a right-hand-side sensitivity analysis vector in the sparse
input format. This replaces the RHSSEN statement.
It is useful when converting
the MPS format into the sparse format of PROC LP.
See
the Converting MPS Format
for more information.
- RANGE
- identifies a range vector in the sparse input format.
This replaces the RANGE statement.
It is useful when converting
the MPS format into the sparse format of PROC LP.
See
the Converting MPS Format
for more information.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.