Code from BasicOptimizer.scala:75 executed in 98.23 seconds (1.690 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: 917479155769100
    Reset training subject: 917480935198800
    Adding measurement 3de08d3d to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.35327547788619995;dx=-2.0709027804559356E-8
    Armijo: th(2.154434690031884)=0.35327547788619995; dx=-2.064105002932715E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.35327547788619995; dx=-2.0641050204120943E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001068668001007879)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.0013892684013102426)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0012289682011590608)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0013091183012346515)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.0013491933512724472)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0013291558262535494)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0013391745887629983)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.0013441839700177227)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.0013416792793903604)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.0013404269340766793)=0.35327547788619995; dx

...skipping 1783 bytes...

    
    Armijo: th(0.0072187862119154605)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006496907590723914)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.006857846901319687)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0066773772460218)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.006767612073670744)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.006722494659846272)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006699935952934036)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.006711215306390154)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.006705575629662095)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.006702755791298066)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    Armijo: th(0.00670134587211605)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006700640912525043)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006700993392320546)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006701169632218298)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006701257752167174)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006701301812141612)=0.35327547788619995; dx=-2.064105021742828E-8 evalInputDelta=0.0
    mu ~= nu (0.006701301812141612): th(0.0)=0.35327547788619995
    Fitness changed from 0.35327547788619995 to 0.35327547788619995
    Static Iteration Total: 44.9435; Orientation: 0.0300; Line Search: 41.5997
    Iteration 2 failed. Error: 0.35327547788619995
    Previous Error: 0.0 -> 0.35327547788619995
    Optimization terminated 2
    Final threshold in iteration 2: 0.35327547788619995 (> -Infinity) after 98.226s (< 720.000s)
    

Returns:

    0.35327547788619995