Package jline.opt

Class LineEvaluator

java.lang.Object

jline.opt.LineEvaluator

public class LineEvaluator
extends Object

Interface between the optimizer and LINE's SolverAUTO. Applies decision variable values to a per-evaluation model copy, solves, and extracts per- (station, class) and system metrics into an EvaluationResult. Mirrors native-Python line_solver.opt.evaluator.LineEvaluator.

Because a copied model carries a cached NetworkStruct that scalar setters do not invalidate, evaluateValues(java.util.Map<java.lang.String, java.lang.Object>) forces refreshStruct after applying variables so the solve reflects the mutated configuration.

Constructor Summary

Constructors

Constructor	Description
LineEvaluator(LayeredNetwork lqnModel, List<DecisionVariable> variables, List<Pair<DecisionVariable,Object>> fixedVariables)
LineEvaluator(Network model, List<DecisionVariable> variables, List<Pair<DecisionVariable,Object>> fixedVariables)

Method Summary

Modifier and Type	Method	Description
Object	copyModel()	Deep copy the base model (flat Network or LayeredNetwork).
Map<String,Object>	decodeVariables(double[] x)	Decode the full vector to a name -> decoded-value map.
EvaluationResult	evaluate(double[] x)
Map<String,Map<String,Double>>	evaluateLayeredSensitivities(Map<String,Object> values)	Compute LQN per-layer service-rate partial sensitivities on demand.
EvaluationResult	evaluateValues(Map<String,Object> values)
EvaluationResult	evaluateValuesWithCache(Map<String,Object> values, Map<String,EvaluationResult> cache)
EvaluationResult	evaluateWithCache(double[] x, Map<String,EvaluationResult> cache)
double[][]	getBounds()	Concatenated per-dimension bounds for the full optimization vector.
int	getEvaluationCount()
List<Pair<DecisionVariable,Object>>	getFixedVariables()
Network	getModel()
int	getTotalDimension()
List<DecisionVariable>	getVariables()
boolean	isLayered()	True if this evaluator drives a LayeredNetwork (LQN) model.
static String	valuesKey(Map<String,Object> values)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

LineEvaluator

public LineEvaluator(Network model,
 List<DecisionVariable> variables,
 List<Pair<DecisionVariable,Object>> fixedVariables)

LineEvaluator

public LineEvaluator(LayeredNetwork lqnModel,
 List<DecisionVariable> variables,
 List<Pair<DecisionVariable,Object>> fixedVariables)

Method Details

getModel

public Network getModel()

isLayered

public boolean isLayered()

True if this evaluator drives a LayeredNetwork (LQN) model.

getVariables

public List<DecisionVariable> getVariables()

getFixedVariables

public List<Pair<DecisionVariable,Object>> getFixedVariables()

getTotalDimension

public int getTotalDimension()

getEvaluationCount

public int getEvaluationCount()

getBounds

public double[][] getBounds()

Concatenated per-dimension bounds for the full optimization vector.

decodeVariables

public Map<String,Object> decodeVariables(double[] x)

Decode the full vector to a name -> decoded-value map.

copyModel

public Object copyModel()

Deep copy the base model (flat Network or LayeredNetwork).

evaluate

public EvaluationResult evaluate(double[] x)

evaluateValues

public EvaluationResult evaluateValues(Map<String,Object> values)

evaluateLayeredSensitivities

public Map<String,Map<String,Double>> evaluateLayeredSensitivities(Map<String,Object> values)

Compute LQN per-layer service-rate partial sensitivities on demand. Rebuilds the configured model copy, solves it with SolverLN, and reshapes SolverLN.getSensitivityTable into {station||jobclass: {metric: d/dRate}}. Called only by the partial-sensitivity gradient path, never on the hot evaluation loop. Returns null on any failure (the caller then finite-differences).

valuesKey

public static String valuesKey(Map<String,Object> values)

evaluateValuesWithCache

public EvaluationResult evaluateValuesWithCache(Map<String,Object> values,
 Map<String,EvaluationResult> cache)

evaluateWithCache

public EvaluationResult evaluateWithCache(double[] x,
 Map<String,EvaluationResult> cache)