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The model argument
Model variables store optimization problems (as described in the problem statement).Models can be built in a number of ways. You can populate the appropriate fields of the model struct using standard MATLAB routines. You can also read a model from a file, using gurobi_read. A few API functions ( gurobi_feasrelax and gurobi_relax) also return models.
Note that all vector fields within the model variable must be dense vectors except for the linear part of the quadratic constraints and INDICATOR general constraints, all matrix fields must be sparse matrices, and all strings, names, etc. must be char arrays.
The following is an enumeration of all of the fields of the model argument that Gurobi will take into account when optimizing the model:
Commonly used fields:
- A
- The linear constraint matrix.
- obj (optional)
- The linear objective vector (the c
vector in the
problem statement).
When present, you must specify one value for each column of
A. When absent, each variable has a default objective
coefficient of 0.
- sense (optional)
- The senses of the linear constraints. Allowed
values are '=', '<', or '>'.
You must specify one value for each row of A, or
a single value to specify that all constraints have the same sense.
When absent, all senses default to '<'.
- rhs (optional)
- The right-hand side vector for the linear
constraints (
in the problem statement). You must specify one value for each row of A. When absent, the right-hand side vector defaults to the zero vector.
- lb (optional)
- The lower bound vector. When present, you must
specify one value for each column of A. When absent, each
variable has a default lower bound of 0.
- ub (optional)
- The upper bound vector. When present, you must
specify one value for each column of A. When absent, the
variables have infinite upper bounds.
- vtype (optional)
- The variable types. This vector is used to
capture variable integrality constraints. Allowed values are
'C' (continuous), 'B' (binary), 'I' (integer), 'S' (semi-continuous), or 'N'
(semi-integer). Binary variables must be either 0 or 1. Integer
variables can take any integer value between the specified lower and
upper bounds. Semi-continuous variables can take any value between
the specified lower and upper bounds, or a value of zero.
Semi-integer variables can take any integer value between the
specified lower and upper bounds, or a value of zero. When present,
you must specify one value for each column of A, or a
single value to specify that all variables have the same type. When
absent, each variable is treated as being continuous. Refer to
this section
for more information on variable types.
- modelsense (optional)
- The optimization sense. Allowed values
are 'min' (minimize) or 'max' (maximize).
When absent, the default optimization sense is minimization.
- modelname (optional)
- The name of the model. The name appears
in the Gurobi log, and when writing a model to a file.
- objcon (optional)
- The constant offset in the objective function
(
in the problem statement).
- varnames (optional)
- The variable names vector. A cell array.
When present, each element of this vector defines the name of a
variable. You must specify a name for each column of A.
- constrnames (optional)
- The constraint names vector. A
cell array. When present, each element of the vector defines the
name of a constraint. You must specify a name for each row of
A.
Quadratic objective and constraint fields:
- Q (optional)
- The quadratic objective matrix. When present,
Q must be a square matrix whose row and column counts are
equal to the number of columns in A.
- quadcon (optional)
- The quadratic constraints. A struct array. When
present, each element in quadcon defines a single quadratic
constraint:
.
The Qc matrix must be a square matrix whose row and column counts are equal to the number of columns of A. There are two options to store the matrix: (i) in model.quadcon(i).Qc as a sparse matrix; (ii) through three dense vectors model.quadcon(i).Qrow, model.quadcon(i).Qcol, and model.quadcon(i).Qval specifying the matrix in triple format, with row indices, column indices, and values, respectively.
The optional q vector defines the linear terms in the constraint. It can be a dense vector specifying a value for each column of A or a sparse vector (sparse n-by-1 matrix). It is stored in model.quadcon(i).q.
The scalar beta is stored in model.quadcon(i).rhs. It defines the right-hand side value for the constraint.
The optional sense string defines the sense of the quadratic constraint. Allowed values are '<', '=' or '>'. If not present, the default sense is '<'. It is stored in model.quadcon(i).sense.
The optional name string defines the name of the quadratic constraint. It is stored in model.quadcon(i).name.
SOS constraint fields:
- sos (optional)
- The Special Ordered Set (SOS) constraints.
A struct array. When present, each entry in sos
defines a single SOS constraint. A SOS constraint can be of type 1 or
2. The type of SOS constraint
is specified via model.sos(i).type. A type 1 SOS constraint is a set of variables where at most one variable in the set may take a value other than zero. A type 2 SOS constraint is an ordered set of variables where at most two variables in the set may take non-zero values. If two take non-zeros values, they must be contiguous in the ordered set. The members of an SOS constraint are specified by placing their indices in vector model.sos(i).index. Weights associated with SOS members are provided in vector model.sos(i).weight. Please refer to this section for details on SOS constraints.
Multi-objective fields:
- multiobj (optional)
- Multi-objective specification for the model.
A struct array. When present, each entry in multiobj defines a
single objective of a multi-objective problem. Please refer to the
Multiple Objectives
section for more details on multi-objective optimization. Each
objective
may have the following fields:
- objn
- Specified via model.multiobj(i).objn. This is the i-th
objective vector.
- objcon (optional)
- Specified via model.multiobj(i).objcon. If provided,
this is the i-th objective constant.
The default value is 0.
- priority (optional)
- Specified via model.multiobj(i).priority. If
provided, this value is the hierarchical priority for this objective.
The default value is 0.
- weight (optional)
- Specified via model.multiobj(i).weight. If provided,
this value is the multiplier used when aggregating objectives.
The default value is 1.0.
- reltol (optional)
- Specified via model.multiobj(i).reltol. If provided,
this value specifies the relative objective degradation when
doing hierarchical multi-objective optimization.
The default value is 0.
- abstol (optional)
- Specified via model.multiobj(i).abstol.
If provided, this value specifies the absolute objective degradation
when doing hierarchical multi-objective optimization.
The default value is 0.
- name (optional)
- Specified via model.multiobj(i).name. If provided, this string specifies the name of the i-th objective function.
Note that when multiple objectives are present, the result.objval field that is returned in the result of an optimization call will be a vector of the same length as model.multiobj.
A multi-objective model can't have other objectives. Thus, combining model.multiobj with any of model.obj, model.objcon, model.pwlobj, or model.Q is an error.
General constraint fields:
The struct arrays described below are used to add general constraints to a model.
Mathematical programming has traditionally defined a set of fundamental constraint types: variable bound constraints, linear constraints, quadratic constraints, integrality constraints, and SOS constraints. These are typically treated directly by the underlying solver (although not always), and are fundamental to the overall algorithm.
Gurobi accepts a number of additional constraint types, which we collectively refer to as general (function) constraints. These are typically not treated directly by the solver. Rather, they are transformed by presolve into constraints (and variables) chosen from among the fundamental types listed above. In some cases, the resulting constraint or constraints are mathematically equivalent to the original; in others, they are approximations. If such constraints appear in your model, but if you prefer to reformulate them yourself using fundamental constraint types instead, you can certainly do so. However, note that Gurobi can sometimes exploit information contained in the other constraints in the model to build a more efficient formulation than what you might create.
The additional constraint types that fall under this general constraint umbrella are:
- MAX (genconmax): set a decision variable equal to the maximum value from among a set of decision variables
- MIN (genconmin): set a decision variable equal to the minimum value from among a set of decision variables
- ABS (genconabs): set a decision variable equal to the absolute value of some other decision variable
- AND (genconand): set a binary variable equal to one if and only if all of a set of binary decision variables are equal to one
- OR (genconor): set a binary variable equal to one if and only if at least one variable out of a set of binary decision variables is equal to one
- INDICATOR (genconind): whenever a given binary variable takes a certain value, then the given linear constraint must be satisfied
- Piecewise-linear constraints (genconpwl): set a variable equal to the piecewise-linear function defined by a set of points using some other variable
- Polynomial (genconpoly): set a variable equal to the polynomial function defined by some other variable
- Natural exponential (genconexp): set a variable equal to the natural exponential function by some other variable
- Exponential (genconexpa): set a variable equal to the exponential function by some other variable
- Natural logarithm (genconlog): set a variable equal to the natural logarithmic function by some other variable
- Logarithm (genconloga): set a variable equal to the logarithmic function by some other variable
- Power (genconpow): set a variable equal to the power function by some other variable
- SIN (genconsin): set a variable equal to the sine function by some other variable
- COS (genconcos): set a variable equal to the cosine function by some other variable
- TAN (gencontan): set a variable equal to the tangent function by some other variable
Please refer to this section for additional details on general constraints.
- genconmax (optional)
- A struct array. When present, each entry in genconmax defines a MAX general constraint of the form
- resvar
- Specified via model.genconmax(i).resvar. Index of the variable in the left-hand side of the constraint.
- vars
- Specified via model.genconmax(i).vars, it is a vector of indices of variables in the right-hand side of the constraint.
- con (optional)
- Specified via model.genconmax(i).con. When present,
specifies the constant on the left-hand side. Default value is
.
- name (optional)
- Specified via model.genconmax(i).name. When present,
specifies the name of the
-th MAX general constraint.
- genconmin (optional)
- A struct array. When present, each entry in genconmax defines a MIN general constraint of the form
- resvar
- Specified via model.genconmin(i).resvar. Index of the variable in the left-hand side of the constraint.
- vars
- Specified via model.genconmin(i).vars, it is a vector of indices of variables in the right-hand side of the constraint.
- con (optional)
- Specified via model.genconmin(i).con. When present,
specifies the constant on the left-hand side. Default value is
.
- name (optional)
- Specified via model.genconmin(i).name. When present,
specifies the name of the
-th MIN general constraint.
- genconabs (optional)
- A struct array. When present, each entry in genconmax defines an ABS general constraint of the form
- resvar
- Specified via model.genconabs(i).resvar. Index of the variable in the left-hand side of the constraint.
- argvar
- Specified via model.genconabs(i).argvar. Index of the variable in the right-hand side of the constraint.
- name (optional)
- Specified via model.genconabs(i).name. When present,
specifies the name of the
-th ABS general constraint.
- genconand (optional)
- A struct array. When present, each entry in genconand defines an AND general constraint of the form
- resvar
- Specified via model.genconand(i).resvar. Index of the variable in the left-hand side of the constraint.
- vars
- Specified via model.genconand(i).vars, it is a vector of indices of variables in the right-hand side of the constraint.
- name (optional)
- Specified via model.genconand(i).name. When present,
specifies the name of the
-th AND general constraint.
- genconor (optional)
- A struct array. When present, each entry in genconor defines an OR general constraint of the form
- resvar
- Specified via model.genconor(i).resvar. Index of the variable in the left-hand side of the constraint.
- vars
- Specified via model.genconor(i).vars, it is a vector of indices of variables in the right-hand side of the constraint.
- name (optional)
- Specified via model.genconor(i).name. When present,
specifies the name of the
-th OR general constraint.
- genconind (optional)
- A struct array. When present, each entry in genconind defines an INDICATOR general constraint of the form
takes the value binval then the linear constraint
must hold. Note that sense is one of '=', '<', or '>' for equality (
), less than or equal (
) or greater than or equal (
) constraints. Each entry may have the following fields:
- binvar
- Specified via model.genconind(i).binvar. Index of the implicating binary variable.
- binval
- Specified via model.genconind(i).binval. Value for the binary variable that forces the following linear constraint to be satisfied. It can be either 0 or 1.
- a
- Specified via model.genconind(i).a. Vector of coefficients of variables participating in the implied linear constraint. You can specify a value for a for each column of A (dense vector) or pass a sparse vector (sparse n-by-1 matrix).
- sense
- Specified via model.genconind(i).sense. Sense of the implied linear constraint. Must be one of '=', '<', or '>'.
- rhs
- Specified via model.genconind(i).rhs. Right-hand side value of the implied linear constraint.
- name (optional)
- Specified via model.genconind(i).name. When present,
specifies the name of the
-th INDICATOR general constraint.
- genconpwl (optional)
- A struct array. When present, each entry in genconpwl defines a
piecewise-linear constraint of the form
are provided as arguments. Refer to the description of piecewise-linear objectives for details of how piecewise-linear functions are defined
Each entry may have the following fields:
- xvar
- Specified via model.genconpwl(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconpwl(i).yvar. Index of the variable in the left-hand side of the constraint.
- xpts
- Specified via model.genconpwl(i).xpts. Specifies the
values for the points that define the piecewise-linear function. Must be in non-decreasing order.
- ypts
- Specified via model.genconpwl(i).ypts. Specifies the
values for the points that define the piecewise-linear function.
- name (optional)
- Specified via model.genconpwl(i).name. When present,
specifies the name of the
-th piecewise-linear general constraint.
- genconpoly (optional)
- A struct array. When present, each entry in genconpoly defines
a polynomial function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconpoly(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconpoly(i).yvar. Index of the variable in the left-hand side of the constraint.
- p
- Specified via model.genconpoly(i).p. Specifies the coefficients for the polynomial
function (starting with the coefficient for the highest power). If
is the highest power term, a dense vector of length
is returned.
- name (optional)
- Specified via model.genconpoly(i).name. When present,
specifies the name of the
-th polynomial function constraint.
- funcpieces (optional)
- Specified via model.genconpoly(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th polynomial function constraint.
- funcpiecelength (optional)
- Specified via model.genconpoly(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th polynomial function constraint.
- funcpieceerror (optional)
- Specified via model.genconpoly(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th polynomial function constraint.
- funcpieceratio (optional)
- Specified via model.genconpoly(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th polynomial function constraint.
- genconexp (optional)
- A struct array. When present, each entry in genconexp defines
the natural exponential function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconexp(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconexp(i).yvar. Index of the variable in the left-hand side of the constraint.
- name (optional)
- Specified via model.genconexp(i).name. When present,
specifies the name of the
-th natural exponential function constraint.
- funcpieces (optional)
- Specified via model.genconexp(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th natural exponential function constraint.
- funcpiecelength (optional)
- Specified via model.genconexp(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th natural exponential function constraint.
- funcpieceerror (optional)
- Specified via model.genconexp(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th natural exponential function constraint.
- funcpieceratio (optional)
- Specified via model.genconexp(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th natural exponential function constraint.
- genconexpa (optional)
- A struct array. When present, each entry in genconexpa defines
an exponential function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconexpa(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconexpa(i).yvar. Index of the variable in the left-hand side of the constraint.
- a
- Specified via model.genconexpa(i).a. Specifies the base of the exponential function
.
- name (optional)
- Specified via model.genconexpa(i).name. When present,
specifies the name of the
-th exponential function constraint.
- funcpieces (optional)
- Specified via model.genconexpa(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th exponential function constraint.
- funcpiecelength (optional)
- Specified via model.genconexpa(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th exponential function constraint.
- funcpieceerror (optional)
- Specified via model.genconexpa(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th exponential function constraint.
- funcpieceratio (optional)
- Specified via model.genconexpa(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th exponential function constraint.
- genconlog (optional)
- A struct array. When present, each entry in genconlog defines
the natural logarithmic function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconlog(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconlog(i).yvar. Index of the variable in the left-hand side of the constraint.
- name (optional)
- Specified via model.genconlog(i).name. When present,
specifies the name of the
-th natural logarithmic function constraint.
- funcpieces (optional)
- Specified via model.genconlog(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th natural logarithmic function constraint.
- funcpiecelength (optional)
- Specified via model.genconlog(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th natural logarithmic function constraint.
- funcpieceerror (optional)
- Specified via model.genconlog(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th natural logarithmic function constraint.
- funcpieceratio (optional)
- Specified via model.genconlog(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th natural logarithmic function constraint.
- genconloga (optional)
- A struct array. When present, each entry in genconloga defines
a logarithmic function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconloga(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconloga(i).yvar. Index of the variable in the left-hand side of the constraint.
- a
- Specified via model.genconloga(i).a. Specifies the base of the logarithmic function
.
- name (optional)
- Specified via model.genconloga(i).name. When present,
specifies the name of the
-th logarithmic function constraint.
- funcpieces (optional)
- Specified via model.genconloga(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th logarithmic function constraint.
- funcpiecelength (optional)
- Specified via model.genconloga(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th logarithmic function constraint.
- funcpieceerror (optional)
- Specified via model.genconloga(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th logarithmic function constraint.
- funcpieceratio (optional)
- Specified via model.genconloga(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th logarithmic function constraint.
- genconpow (optional)
- A struct array. When present, each entry in genconpow defines
a power function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconpow(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconpow(i).yvar. Index of the variable in the left-hand side of the constraint.
- a
- Specified via model.genconpow(i).a. Specifies the exponent of the power function.
- name (optional)
- Specified via model.genconpow(i).name. When present,
specifies the name of the
-th power function constraint.
- funcpieces (optional)
- Specified via model.genconpow(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th power function constraint.
- funcpiecelength (optional)
- Specified via model.genconpow(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th power function constraint.
- funcpieceerror (optional)
- Specified via model.genconpow(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th power function constraint.
- funcpieceratio (optional)
- Specified via model.genconpow(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th power function constraint.
- genconsin (optional)
- A struct array. When present, each entry in genconsin defines
the sine function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconsin(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconsin(i).yvar. Index of the variable in the left-hand side of the constraint.
- name (optional)
- Specified via model.genconsin(i).name. When present,
specifies the name of the
-th sine function constraint.
- funcpieces (optional)
- Specified via model.genconsin(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th sine function constraint.
- funcpiecelength (optional)
- Specified via model.genconsin(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th sine function constraint.
- funcpieceerror (optional)
- Specified via model.genconsin(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th sine function constraint.
- funcpieceratio (optional)
- Specified via model.genconsin(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th sine function constraint.
- genconcos (optional)
- A struct array. When present, each entry in genconcos defines
the cosine function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.genconcos(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.genconcos(i).yvar. Index of the variable in the left-hand side of the constraint.
- name (optional)
- Specified via model.genconcos(i).name. When present,
specifies the name of the
-th cosine function constraint.
- funcpieces (optional)
- Specified via model.genconcos(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th cosine function constraint.
- funcpiecelength (optional)
- Specified via model.genconcos(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th cosine function constraint.
- funcpieceerror (optional)
- Specified via model.genconcos(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th cosine function constraint.
- funcpieceratio (optional)
- Specified via model.genconcos(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th cosine function constraint.
- gencontan (optional)
- A struct array. When present, each entry in gencontan defines
the tangent function constraint of the form
Each entry may have the following fields:
- xvar
- Specified via model.gencontan(i).xvar. Index of the variable in the right-hand side of the constraint.
- yvar
- Specified via model.gencontan(i).yvar. Index of the variable in the left-hand side of the constraint.
- name (optional)
- Specified via model.gencontancos(i).name. When present,
specifies the name of the
-th tangent function constraint.
- funcpieces (optional)
- Specified via model.gencontan(i).funcpieces. When present, specifies the
FuncPieces attribute of the
-th tangent function constraint.
- funcpiecelength (optional)
- Specified via model.gencontan(i).funcpiecelength. When present, specifies the
FuncPieceLength attribute of the
-th tangent function constraint.
- funcpieceerror (optional)
- Specified via model.gencontan(i).funcpieceerror. When present, specifies the
FuncPieceError attribute of the
-th tangent function constraint.
- funcpieceratio (optional)
- Specified via model.gencontan(i).funcpieceratio. When present, specifies the
FuncPieceRatio attribute of the
-th tangent function constraint.
Advanced fields:
- pwlobj (optional)
- The piecewise-linear objective functions.
A struct array. When present, each entry in pwlobj
defines a piecewise-linear objective function for a single variable.
The index of the variable whose objective function is being defined is
stored in model.pwlobj(i).var. The
values for the points that define the piecewise-linear function are stored in
model.pwlobj(i).x. The values in thevector must be in non-decreasing order. The
values for the points that define the piecewise-linear function are stored in model.pwlobj(i).y.
- vbasis (optional)
- The variable basis status vector. Used to
provide an advanced starting point for the simplex algorithm. You
would generally never concern yourself with the contents of this
vector, but would instead simply pass it from the result of a previous
optimization run to the input of a subsequent run. When present, you
must specify one value for each column of A.
- cbasis (optional)
- The constraint basis status vector. Used to
provide an advanced starting point for the simplex algorithm. Consult
the vbasis description for details. When present, you
must specify one value for each row of A.
- varhintval (optional)
- A set of user hints. If you know that a
variable is likely to take a particular value in high quality
solutions of a MIP model, you can provide that value as a hint. You
can also (optionally) provide information about your level of
confidence in a hint with the
varhintpri field.
If present, you must specify one value
for each column of A.
Use a value of nan for variables where no such hint is known.
For more details, please refer to the
VarHitVal attribute documentation.
- varhintpri (optional)
- Priorities on user hints. After
providing variable hints through the varhintval struct,
you can optionally also provide hint priorities to give an indication
of your level of confidence in your hints.
If present, you must specify a value
for each column of A.
For more details, please
refer to the VarHintPri attribute
documentation.
- branchpriority (optional)
- Variable branching priority. If
present, the value of this attribute is used as the primary criteria
for selecting a fractional variable for branching during the MIP
search. Variables with larger values always take priority over those
with smaller values. Ties are broken using the standard branch
variable selection criteria.
If present, you must specify one value
for each column of A.
- pstart (optional)
- The current simplex start vector. If you set
pstart values for every variable in the model and
dstart values for every constraint, then simplex will use
those values to compute a warm start basis. For more details, please
refer to the PStart attribute
documentation.
- dstart (optional)
- The current simplex start vector. If you set
dstart values for every linear constraint in the model and
pstart values for every variable, then
simplex will use those values to compute a warm start basis. For more
details, please refer to the DStart
attribute documentation.
- lazy (optional)
- Determines whether a linear constraint is
treated as a lazy constraint.
If present, you must specify one value for each row of A.
For more details, please refer
to the Lazy attribute documentation.
- start (optional)
- The MIP start vector. The MIP solver will
attempt to build an initial solution from this vector. When present,
you must specify a start value for each variable. Note that you can
set the start value for a variable to nan, which
instructs the MIP solver to try to fill in a value for that variable.
- partition (optional)
- The MIP variable partition number, which
is used by the MIP solution improvement heuristic. If present,
you must specify one value for each variable of A.
For more details, please refer
to the Partition
attribute documentation.
If any of the mandatory components listed above are missing, the gurobi() function will return an error.
Below is an example that demonstrates the construction of a simple
optimization model:
model.A = sparse([1 2 3; 1 1 0]);
model.obj = [1 1 1];
model.modelsense = 'max';
model.rhs = [4; 1];
model.sense = '<>'
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