Code from BasicOptimizer.scala:75 executed in 346.35 seconds (4.850 gc):

    val lineSearchInstance: LineSearchStrategy = lineSearchFactory
    IterativeTrainer.wrap(trainable)
      .setOrientation(orientation())
      .setMonitor(new TrainingMonitor() {
        override def clear(): Unit = trainingMonitor.clear()
  
        override def log(msg: String): Unit = trainingMonitor.log(msg)
  
        override def onStepFail(currentPoint: Step): Boolean = {
          BasicOptimizer.this.onStepFail(trainable, currentPoint)
        }
  
        override def onStepComplete(currentPoint: Step): Unit = {
          if (0 < logEvery && (0 == currentPoint.iteration % logEvery || currentPoint.iteration < logEvery)) {
            val image = currentImage
            timelineAnimation += image
            val caption = "Iteration " + currentPoint.iteration
            out.p(caption + "\n" + out.jpg(image, caption))
          }
          BasicOptimizer.this.onStepComplete(trainable, currentPoint)
          trainingMonitor.onStepComplete(currentPoint)
          super.onStepComplete(currentPoint)
        }
      })
      .setTimeout(trainingMinutes, TimeUnit.MINUTES)
      .setMaxIterations(trainingIterations)
      .setLineSearchFactory((_: CharSequence) => lineSearchInstance)
      .setTerminateThreshold(java.lang.Double.NEGATIVE_INFINITY)
      .runAndFree
      .asInstanceOf[lang.Double]

Logging:

    Reset training subject: 905656407890000
    Reset training subject: 905661862686300
    Adding measurement 2ba9765b to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.1717672199010849;dx=-1.3627597210124332E-9
    Armijo: th(2.154434690031884)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.006732608406349637)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.008602777408113426)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.00953786190899532)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.010005404159436267)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.01023917528465674)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.010122289722046504)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.010180732503351622)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.01020995389400418)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.010195343198677901)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.010188037851014763)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.010184385177183192)=0.1717672199010849; dx=-1.3000041

...skipping 1627 bytes...

    utDelta=0.0
    Armijo: th(0.05484983190212065)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.04936484871190859)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.052107340307014624)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.05073609450946161)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.051421717408238116)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.05107890595884986)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.05090750023415573)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.05099320309650279)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.05095035166532926)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.05092892594974249)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    Armijo: th(0.05091821309194911)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.050912856663052425)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.050915534877500765)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.05091687398472494)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.05091754353833702)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.050917878315143064)=0.1717672199010849; dx=-1.3000041456733387E-9 evalInputDelta=0.0
    mu ~= nu (0.050917878315143064): th(0.0)=0.1717672199010849
    Fitness changed from 0.1717672199010849 to 0.1717672199010849
    Static Iteration Total: 168.3886; Orientation: 0.0653; Line Search: 155.3315
    Iteration 2 failed. Error: 0.1717672199010849
    Previous Error: 0.0 -> 0.1717672199010849
    Optimization terminated 2
    Final threshold in iteration 2: 0.1717672199010849 (> -Infinity) after 346.355s (< 1800.000s)
    

Returns:

    0.1717672199010849