Enum grb::attribute::ModelDoubleAttr[−][src]

pub enum ModelDoubleAttr {
Show 64 variants    BoundSVio,
    BoundSVioSum,
    BoundVio,
    BoundVioSum,
    ComplVio,
    ComplVioSum,
    ConstrResidual,
    ConstrResidualSum,
    ConstrSResidual,
    ConstrSResidualSum,
    ConstrSVio,
    ConstrSVioSum,
    ConstrVio,
    ConstrVioSum,
    DNumNZs,
    DualResidual,
    DualResidualSum,
    DualSResidual,
    DualSResidualSum,
    DualSVio,
    DualSVioSum,
    DualVio,
    DualVioSum,
    FarkasProof,
    IntVio,
    IntVioSum,
    IterCount,
    Kappa,
    KappaExact,
    MIPGap,
    MaxBound,
    MaxCoeff,
    MaxObjCoeff,
    MaxQCCoeff,
    MaxQCLCoeff,
    MaxQCRHS,
    MaxQObjCoeff,
    MaxRHS,
    MinBound,
    MinCoeff,
    MinObjCoeff,
    MinQCCoeff,
    MinQCLCoeff,
    MinQCRHS,
    MinQObjCoeff,
    MinRHS,
    NodeCount,
    ObjBound,
    ObjBoundC,
    ObjCon,
    ObjNAbsTol,
    ObjNCon,
    ObjNRelTol,
    ObjNVal,
    ObjNWeight,
    ObjVal,
    PoolObjBound,
    PoolObjVal,
    Runtime,
    ScenNObjBound,
    ScenNObjVal,
    MaxVio,
    OpenNodeCount,
    Work,
}

Variants

BoundSVio

Modifiable: No
Type: double (f64)

Maximum (scaled) bound violation.

Only available for continuous models.

Reference manual.

BoundSVioSum

Modifiable: No
Type: double (f64)

Sum of (scaled) bound violations.

Only available for continuous models.

Reference manual.

BoundVio

Modifiable: No
Type: double (f64)

Maximum (unscaled) bound violation.

Available for all model types.

Reference manual.

BoundVioSum

Modifiable: No
Type: double (f64)

Sum of (unscaled) bound violations.

Available for all model types.

Reference manual.

ComplVio

Modifiable: No
Type: double (f64)

Maximum complementarity violation. In an optimal solution, the product of the value of a variable and its reduced cost must be zero. This isn’t always strictly true for interior point solutions. This attribute returns the maximum complementarity violation for any variable.

Only available for continuous models.

Reference manual.

ComplVioSum

Modifiable: No
Type: double (f64)

Sum of complementarity violation.

Only available for continuous models.

Reference manual.

ConstrResidual

Modifiable: No
Type: double (f64)

Reporting constraint violations for the simplex solver is actually more complex than it may appear, due to the treatment of slacks on linear inequality constraints. The simplex solver introduces explicit non-negative slack variables inside the algorithm. Thus, for example, $a^Tx \le b$ becomes $a^Tx + s = b$. In this formulation, constraint errors can show up in two places: (i) as bound violations on the computed slack variable values, and (ii) as differences between $a^Tx + s$ and $b$. We report the former as ConstrVio and the latter as ConstrResidual.

Only available for continuous models. For MIP models, constraint violations are reported in ConstrVio.

Reference manual.

ConstrResidualSum

Modifiable: No
Type: double (f64)

Sum of (unscaled) linear constraint violations.

Only available for continuous models.

Reference manual.

ConstrSResidual

Modifiable: No
Type: double (f64)

Maximum (scaled) primal constraint error.

Only available for continuous models.

Reference manual.

ConstrSResidualSum

Modifiable: No
Type: double (f64)

Sum of (scaled) linear constraint violations.

Only available for continuous models.

Reference manual.

ConstrSVio

Modifiable: No
Type: double (f64)

Maximum (scaled) slack bound violation.

Only available for continuous models.

Reference manual.

ConstrSVioSum

Modifiable: No
Type: double (f64)

Sum of (scaled) slack bound violations.

Only available for continuous models.

Reference manual.

ConstrVio

Modifiable: No
Type: double (f64)

Reporting constraint violations for the simplex solver is actually more complex than it may appear, due to the treatment of slacks on linear inequality constraints. The simplex solver introduces explicit non-negative slack variables inside the algorithm. Thus, for example, $a^Tx \le b$ becomes $a^Tx + s = b$. In this formulation, constraint errors can show up in two places: (i) as bound violations on the computed slack variable values, and (ii) as differences between $a^Tx + s$ and $b$. We report the former as ConstrVio and the latter as ConstrResidual.

For MIP models, the maximum violation of the constraints, including linear, quadratic, SOS and general constraints, is reported in ConstrVio.

Available for all model types.

Reference manual.

ConstrVioSum

Modifiable: No
Type: double (f64)

Sum of (unscaled) slack bound violations.

Available for all model types.

Reference manual.

DNumNZs

Modifiable: No
Type: double (f64)

The number of non-zero coefficients in the linear constraints of the model. This attribute is provided in double precision format to accurately count the number of non-zeros in models that contain more than 2 billion non-zero coefficients.

Reference manual.

DualResidual

Modifiable: No
Type: double (f64)

Reporting dual constraint violations for the simplex solver is actually more complex than it may appear, due to the treatment of reduced costs for bounded variables. The simplex solver introduces explicit non-negative reduced-cost variables inside the algorithm. Thus, $a^Ty \ge c$ becomes $a^Ty - z = c$ (where $y$ is the dual vector and $z$ is the reduced cost). In this formulation, errors can show up in two places: (i) as bound violations on the computed reduced- cost variable values, and (ii) as differences between $a^Ty - z$ and $c$. We report the former as DualVio and the latter as DualResidual.

DualResidual reports the maximum (unscaled) dual constraint error.

Only available for continuous models.

Reference manual.

DualResidualSum

Modifiable: No
Type: double (f64)

Sum of (unscaled) dual constraint errors.

Only available for continuous models.

Reference manual.

DualSResidual

Modifiable: No
Type: double (f64)

Maximum (scaled) dual constraint error.

Only available for continuous models.

Reference manual.

DualSResidualSum

Modifiable: No
Type: double (f64)

Sum of (scaled) dual constraint errors.

Only available for continuous models.

Reference manual.

DualSVio

Modifiable: No
Type: double (f64)

Maximum (scaled) reduced cost violation.

Only available for continuous models.

Reference manual.

DualSVioSum

Modifiable: No
Type: double (f64)

Sum of (scaled) reduced cost violations.

Only available for continuous models.

Reference manual.

DualVio

Modifiable: No
Type: double (f64)

Reporting dual constraint violations for the simplex solver is actually more complex than it may appear, due to the treatment of reduced costs for bounded variables. The simplex solver introduces explicit non-negative reduced-cost variables inside the algorithm. Thus, $a^Ty \ge c$ becomes $a^Ty - z = c$ (where $y$ is the dual vector and $z$ is the reduced cost). In this formulation, errors can show up in two places: (i) as bound violations on the computed reduced- cost variable values, and (ii) as differences between $a^Ty - z$ and $c$. We report the former as DualVio and the latter as DualResidual.

DualVio reports the maximum (unscaled) reduced-cost bound violation.

Only available for continuous models.

Reference manual.

DualVioSum

Modifiable: No
Type: double (f64)

Sum of (unscaled) reduced cost violations.

Only available for continuous models.

Reference manual.

FarkasProof

Modifiable: No
Type: double (f64)

Together, attributes FarkasDual and FarkasProof provide a certificate of infeasibility for the given infeasible problem. Specifically, FarkasDual can be used to form the following inequality from the original constraints that is infeasible within the bounds of the variables:

$\lambda^tAx \leq \lambda^tb.$

This Farkas constraint is valid, because $\lambda_i \geq 0$ if the $i$-th constraint has a $\leq$ sense and $\lambda_i \leq 0$ if the $i$-th constraint has a $\geq$ sense.

Let

$\bar{a} := \lambda^tA$

be the coefficients of this inequality and

$\bar{b} := \lambda^tb$

be its right hand side. With $L_j$ and $U_j$ being the lower and upper bounds of the variables $x_j$ we have $\bar{a}_j \geq 0$ if $U_j = \infty$, and $\bar{a}_j \leq 0$ if $L_j = -\infty$.

The minimum violation of the Farkas constraint is achieved by setting $x^_j := L_j$ for $\bar{a}_j > 0$ and $x^_j := U_j$ for $\bar{a}_j < 0$. Then, we can calculate the minimum violation as

$\beta := \bar{a}^tx^* - \bar{b} = \sum\limits_{j:\bar{a}_j>0}\bar{a}jL_j + \sum\limits{j:\bar{a}_j<0}\bar{a}_jU_j - \bar{b}$

where $\beta>0$.

The FarkasProof attribute provides $\beta$, and the FarkasDual attributes provide the $\lambda$ multipliers for the original constraints.

These attributes are only available when parameter InfUnbdInfo is set to 1.

Reference manual.

IntVio

Modifiable: No
Type: double (f64)

A MIP solver won’t always assign strictly integral values to integer variables. This attribute returns the largest distance between the computed value of any integer variable and the nearest integer.

Only available for MIP models.

Reference manual.

IntVioSum

Modifiable: No
Type: double (f64)

Sum of integrality violations.

Only available for MIP models.

Reference manual.

IterCount

Modifiable: No
Type: double (f64)

Number of simplex iterations performed during the most recent optimization.

Reference manual.

Kappa

Modifiable: No
Type: double (f64)

Estimated condition number for the current LP basis matrix. Only available for basic solutions.

Reference manual.

KappaExact

Modifiable: No
Type: double (f64)

Exact condition number for the current LP basis matrix. Only available for basic solutions. The exact condition number is much more expensive to compute than the estimate that you get from the Kappa attribute. Only available for basic solutions.

Reference manual.

MIPGap

Modifiable: No
Type: double (f64)

Current relative MIP optimality gap; computed as

$\vert ObjBound-ObjVal\vert/\vert ObjVal\vert$ (where ObjBound and ObjVal are the MIP objective bound and incumbent solution objective, respectively. Returns GRB_INFINITY when an incumbent solution has not yet been found, when no objective bound is available, or when the current incumbent objective is 0.

Reference manual.

MaxBound

Modifiable: No
Type: double (f64)

Maximum (finite) variable bound.

Reference manual.

MaxCoeff

Modifiable: No
Type: double (f64)

Maximum matrix coefficient (in absolute value) in the linear constraint matrix.

Reference manual.

MaxObjCoeff

Modifiable: No
Type: double (f64)

Maximum linear objective coefficient (in absolute value).

Reference manual.

MaxQCCoeff

Modifiable: No
Type: double (f64)

Maximum coefficient in the quadratic part of all quadratic constraint matrices (in absolute value).

Reference manual.

MaxQCLCoeff

Modifiable: No
Type: double (f64)

Maximum coefficient in the linear part of all quadratic constraint matrices (in absolute value).

Reference manual.

MaxQCRHS

Modifiable: No
Type: double (f64)

Maximum (finite) quadratic constraint right-hand side value (in absolute value).

Reference manual.

MaxQObjCoeff

Modifiable: No
Type: double (f64)

Maximum coefficient of the quadratic terms in the objective (in absolute value).

Reference manual.

MaxRHS

Modifiable: No
Type: double (f64)

Maximum (finite) linear constraint right-hand side value (in absolute value).

Reference manual.

MinBound

Modifiable: No
Type: double (f64)

Minimum (non-zero) variable bound.

Reference manual.

MinCoeff

Modifiable: No
Type: double (f64)

Minimum non-zero matrix coefficient (in absolute value) in the linear constraint matrix.

Reference manual.

MinObjCoeff

Modifiable: No
Type: double (f64)

Minimum (non-zero) linear objective coefficient (in absolute value).

Reference manual.

MinQCCoeff

Modifiable: No
Type: double (f64)

Minimum (non-zero) coefficient in the quadratic part of all quadratic constraint matrices (in absolute value).

Reference manual.

MinQCLCoeff

Modifiable: No
Type: double (f64)

Minimum (non-zero) coefficient in the linear part of all quadratic constraint matrices (in absolute value).

Reference manual.

MinQCRHS

Modifiable: No
Type: double (f64)

Minimum (non-zero) quadratic constraint right-hand side value (in absolute value).

Reference manual.

MinQObjCoeff

Modifiable: No
Type: double (f64)

Minimum (non-zero) coefficient of the quadratic terms in the objective (in absolute value).

Reference manual.

MinRHS

Modifiable: No
Type: double (f64)

Minimum (non-zero) linear constraint right-hand side value (in absolute value).

Reference manual.

NodeCount

Modifiable: No
Type: double (f64)

Number of branch-and-cut nodes explored in the most recent optimization.

Reference manual.

ObjBound

Modifiable: No
Type: double (f64)

The best known bound on the optimal objective. When solving a MIP model, the algorithm maintains both a lower bound and an upper bound on the optimal objective value. For a minimization model, the upper bound is the objective of the best known feasible solution, while the lower bound gives a bound on the best possible objective.

In contrast to ObjBoundC, this attribute takes advantage of objective integrality information to round to a tighter bound. For example, if the objective is known to take an integral value and the current best bound is 1.5, ObjBound will return 2.0 while ObjBoundC will return 1.5.

Reference manual.

ObjBoundC

Modifiable: No
Type: double (f64)

The best known bound on the optimal objective. When solving a MIP model, the algorithm maintains both a lower bound and an upper bound on the optimal objective value. For a minimization model, the upper bound is the objective of the best known feasible solution, while the lower bound gives a bound on the best possible objective.

In contrast to ObjBound, this attribute does not take advantage of objective integrality information to round to a tighter bound. For example, if the objective is known to take an integral value and the current best bound is 1.5, ObjBound will return 2.0 while ObjBoundC will return 1.5.

Reference manual.

ObjCon

Modifiable: Yes
Type: double (f64)

A constant value that is added into the model objective. The default value is 0.

Reference manual.

ObjNAbsTol

Modifiable: Yes
Type: double (f64)

This attribute is used to set the allowable degradation for objective $n$ when doing hierarchical multi-objective optimization. You set $n$ using the ObjNumber parameter.

Hierarchical multi-objective MIP optimization will optimize for the different objectives in the model one at a time, in priority order. If it achieves objective value $z$ when it optimizes for this objective, then subsequent steps are allowed to degrade this value by at most ObjNAbsTol.

Objective degradations are handled differently for multi-objective LP models. For LP models, solution quality for higher-priority objectives is maintained by fixing some variables to their values in previous optimal solutions. These fixings are decided using variable reduced costs. The value of the ObjNAbsTol parameter indicates the amount by which a fixed variable’s reduced cost is allowed to violate dual feasibility. The value of the related ObjNRelTol attribute is ignored.

The default absolute tolerance for an objective is 1e-6.

The number of objectives in the model can be queried (or modified) using the NumObj attribute.

Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

Reference manual.

ObjNCon

Modifiable: Yes
Type: double (f64)

When the model has multiple objectives, this attribute is used to query or modify the constant term for objective $n$. You set $n$ using the ObjNumber parameter. Note that when ObjNumber is equal to 0, ObjNCon is equivalent to ObjCon.

The number of objectives in the model can be queried (or modified) using the NumObj attribute.

Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

Reference manual.

ObjNRelTol

Modifiable: Yes
Type: double (f64)

This attribute is used to set the allowable degradation for objective $n$ when doing hierarchical multi-objective optimization for MIP models. You set $n$ using the ObjNumber parameter.

Hierarchical multi-objective MIP optimization will optimize for the different objectives in the model one at a time, in priority order. If it achieves objective value $z$ when it optimizes for this objective, then subsequent steps are allowed to degrade this value by at most ObjNRelTol*$\vert z\vert$.

Objective degradations are handled differently for multi-objective LP models. The allowable degradation is controlled strictly using the ObjNAbsTol.

The default relative tolerance for an objective is 0.

The number of objectives in the model can be queried (or modified) using the NumObj attribute.

Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

Reference manual.

ObjNVal

Modifiable: No
Type: double (f64)

This attribute is used to query the objective value obtained for objective $n$ by the $k$-th solution stored in the pool of feasible solutions found so far for the problem. You set $n$ using the ObjNumber parameter, while you set $k$ using the SolutionNumber parameter.

The number of objectives in the model can be queried (or modified) using the NumObj attribute; while the number of stored solutions can be queried using the SolCount attribute.

Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

Reference manual.

ObjNWeight

Modifiable: Yes
Type: double (f64)

This attribute is used to query or modify the weight of objective $n$ when doing blended multi-objective optimization. You set $n$ using the ObjNumber parameter.

The default weight for an objective is 1.0.

The number of objectives in the model can be queried (or modified) using the NumObj attribute.

Please refer to the discussion of Multiple Objectives for more information on the use of alternative objectives.

Reference manual.

ObjVal

Modifiable: No
Type: double (f64)

The objective value for the current solution. If the model was solved to optimality, then this attribute gives the optimal objective value.

Reference manual.

PoolObjBound

Modifiable: No
Type: double (f64)

Bound on the objective of undiscovered MIP solutions. The MIP solver stores solutions that it finds during the MIP search, but it only provides quality guarantees for those whose objective is at least as good as PoolObjBound. Specifically, further exploration of the MIP search tree will not find solutions whose objective is better than PoolObjBound.

The difference between PoolObjBound and ObjBound is that the former gives an objective bound for undiscovered solutions, while the latter gives a bound for any solution. Note that PoolObjBound and ObjBound can only have different values if parameter PoolSearchMode is set to 2.

Please consult the section on Solution Pools for a more detailed discussion of this topic.

Reference manual.

PoolObjVal

Modifiable: No
Type: double (f64)

This attribute is used to query the objective value of the $k$-th solution stored in the pool of feasible solutions found so far for the problem. You set $k$ using the SolutionNumber parameter.

The number of stored solutions can be queried using the SolCount attribute.

Please consult the section on Solution Pools for a more detailed discussion of this topic.

Reference manual.

Runtime

Modifiable: No
Type: double (f64)

Runtime for the most recent optimization (in seconds). Note that all times reported by the Gurobi Optimizer are wall- clock times.

Reference manual.

ScenNObjBound

Modifiable: No
Type: double (f64)

When the model has multiple scenarios, this attribute is used to query the objective bound for scenario $n$. You set $n$ using the ScenarioNumber parameter.

The number of scenarios in the model can be queried (or modified) using the NumScenarios attribute.

Please refer to the Multiple Scenarios discussion for more information.

Reference manual.

ScenNObjVal

Modifiable: No
Type: double (f64)

When the model has multiple scenarios, this attribute is used to query the objective value of the current solution for scenario $n$. You set $n$ using the ScenarioNumber parameter. If no solution is available, this returns GRB_INFINITY (for a minimization objective).

The number of scenarios in the model can be queried (or modified) using the NumScenarios attribute.

Please refer to the Multiple Scenarios discussion for more information.

Reference manual.

MaxVio

Modifiable: No
Type: double (f64)

Maximum of all (unscaled) violations that apply to model type.

Reference manual.

OpenNodeCount

Modifiable: No
Type: double (f64)

Number of open branch-and-cut nodes at the end of the most recent optimization. An open node is one that has been created but not yet explored.

Reference manual.

Work

Modifiable: No
Type: double (f64)

Work spent on the most recent optimization. In contrast to Runtime, work is deterministic, meaning that you will get exactly the same result every time provided you solve the same model on the same hardware with the same parameter and attribute settings. The units on this metric are arbitrary. One work unit corresponds very roughly to one second on a single thread, but this greatly depends on the hardware on which Gurobi is running and the model that is being solved.

Reference manual.