All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

SolveLogOuterClass.InfeasibilityInformation, SolveLogOuterClass.InfeasibilityInformation.Builder

Enclosing class:

SolveLogOuterClass
```
public static interface SolveLogOuterClass.InfeasibilityInformationOrBuilder
extends com.google.protobuf.MessageOrBuilder
```

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`SolveLogOuterClass.PointType`	`getCandidateType()`	Type of the point used to compute the InfeasibilityInformation.
`double`	`getDualRayObjective()`	The objective of the linear program labeled (1) in the previous paragraph.
`double`	`getMaxDualRayInfeasibility()`	Let (y_ray, r_ray) be the algorithm's estimate of the dual and reduced cost extreme ray where (y_ray, r_ray) is a vector scaled such that its infinity norm is one.
`double`	`getMaxPrimalRayInfeasibility()`	Let x_ray be the algorithm's estimate of the primal extreme ray where x_ray is a vector scaled such that its infinity norm is one.
`double`	`getPrimalRayLinearObjective()`	The value of the linear part of the primal objective (ignoring additive constants) evaluated at x_ray, i.e., c' * x_ray where c is the objective coefficient vector.
`double`	`getPrimalRayQuadraticNorm()`	The l_∞ norm of the vector resulting from taking the quadratic matrix from primal objective and multiplying it by the primal variables.
`boolean`	`hasCandidateType()`	Type of the point used to compute the InfeasibilityInformation.
`boolean`	`hasDualRayObjective()`	The objective of the linear program labeled (1) in the previous paragraph.
`boolean`	`hasMaxDualRayInfeasibility()`	Let (y_ray, r_ray) be the algorithm's estimate of the dual and reduced cost extreme ray where (y_ray, r_ray) is a vector scaled such that its infinity norm is one.
`boolean`	`hasMaxPrimalRayInfeasibility()`	Let x_ray be the algorithm's estimate of the primal extreme ray where x_ray is a vector scaled such that its infinity norm is one.
`boolean`	`hasPrimalRayLinearObjective()`	The value of the linear part of the primal objective (ignoring additive constants) evaluated at x_ray, i.e., c' * x_ray where c is the objective coefficient vector.
`boolean`	`hasPrimalRayQuadraticNorm()`	The l_∞ norm of the vector resulting from taking the quadratic matrix from primal objective and multiplying it by the primal variables.

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder
isInitialized

Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Detail

hasMaxPrimalRayInfeasibility

boolean hasMaxPrimalRayInfeasibility()

 Let x_ray be the algorithm's estimate of the primal extreme ray where x_ray
 is a vector scaled such that its infinity norm is one. A simple and typical
 choice of x_ray is x_ray = x / | x |_∞ where x is the current primal
 iterate. For this value compute the maximum absolute error in the primal
 linear program with the right hand side and finite variable bounds set to
 zero. This error refers to both the linear constraints and sign constraints
 on the ray.

optional double max_primal_ray_infeasibility = 1;

Returns:: Whether the maxPrimalRayInfeasibility field is set.

getMaxPrimalRayInfeasibility

double getMaxPrimalRayInfeasibility()

 Let x_ray be the algorithm's estimate of the primal extreme ray where x_ray
 is a vector scaled such that its infinity norm is one. A simple and typical
 choice of x_ray is x_ray = x / | x |_∞ where x is the current primal
 iterate. For this value compute the maximum absolute error in the primal
 linear program with the right hand side and finite variable bounds set to
 zero. This error refers to both the linear constraints and sign constraints
 on the ray.

optional double max_primal_ray_infeasibility = 1;

Returns:: The maxPrimalRayInfeasibility.

hasPrimalRayLinearObjective

boolean hasPrimalRayLinearObjective()

 The value of the linear part of the primal objective (ignoring additive
 constants) evaluated at x_ray, i.e., c' * x_ray where c is the objective
 coefficient vector.

optional double primal_ray_linear_objective = 2;

Returns:: Whether the primalRayLinearObjective field is set.

getPrimalRayLinearObjective

double getPrimalRayLinearObjective()

 The value of the linear part of the primal objective (ignoring additive
 constants) evaluated at x_ray, i.e., c' * x_ray where c is the objective
 coefficient vector.

optional double primal_ray_linear_objective = 2;

Returns:: The primalRayLinearObjective.

hasPrimalRayQuadraticNorm

boolean hasPrimalRayQuadraticNorm()

 The l_∞ norm of the vector resulting from taking the quadratic matrix from
 primal objective and multiplying it by the primal variables. For linear
 programming problems this is zero.

optional double primal_ray_quadratic_norm = 3;

Returns:: Whether the primalRayQuadraticNorm field is set.

getPrimalRayQuadraticNorm

double getPrimalRayQuadraticNorm()

 The l_∞ norm of the vector resulting from taking the quadratic matrix from
 primal objective and multiplying it by the primal variables. For linear
 programming problems this is zero.

optional double primal_ray_quadratic_norm = 3;

Returns:: The primalRayQuadraticNorm.

hasMaxDualRayInfeasibility

boolean hasMaxDualRayInfeasibility()

 Let (y_ray, r_ray) be the algorithm's estimate of the dual and reduced cost
 extreme ray where (y_ray, r_ray) is a vector scaled such that its infinity
 norm is one. A simple and typical choice of y_ray is
 (y_ray, r_ray) = (y, r) / max(| y |_∞, | r |_∞) where y is the
 current dual iterate and r is the current dual reduced costs.
 Consider the quadratic program we are solving but with the objective (both
 quadratic and linear terms) set to zero. This forms a linear program
 (label this linear program (1)) with no objective. Take the dual of (1) and
 compute the maximum absolute value of the constraint error for
 (y_ray, r_ray) to obtain the value of max_dual_ray_infeasibility.

optional double max_dual_ray_infeasibility = 4;

Returns:: Whether the maxDualRayInfeasibility field is set.

getMaxDualRayInfeasibility

double getMaxDualRayInfeasibility()

 Let (y_ray, r_ray) be the algorithm's estimate of the dual and reduced cost
 extreme ray where (y_ray, r_ray) is a vector scaled such that its infinity
 norm is one. A simple and typical choice of y_ray is
 (y_ray, r_ray) = (y, r) / max(| y |_∞, | r |_∞) where y is the
 current dual iterate and r is the current dual reduced costs.
 Consider the quadratic program we are solving but with the objective (both
 quadratic and linear terms) set to zero. This forms a linear program
 (label this linear program (1)) with no objective. Take the dual of (1) and
 compute the maximum absolute value of the constraint error for
 (y_ray, r_ray) to obtain the value of max_dual_ray_infeasibility.

optional double max_dual_ray_infeasibility = 4;

Returns:: The maxDualRayInfeasibility.

hasDualRayObjective

boolean hasDualRayObjective()

 The objective of the linear program labeled (1) in the previous paragraph.

optional double dual_ray_objective = 5;

Returns:: Whether the dualRayObjective field is set.

getDualRayObjective

double getDualRayObjective()

 The objective of the linear program labeled (1) in the previous paragraph.