Code from BasicOptimizer.scala:75 executed in 45.73 seconds (1.906 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: 1642496627462
    Reset training subject: 1643215994454
    Adding measurement 433edb56 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.0085379016458575;dx=-5.9869545135421945E-9
    Armijo: th(2.154434690031884)=1.0085379016458575; dx=-5.986941289214788E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.0085379016458575; dx=-5.986828044284655E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.0085379016458575; dx=-5.986826882089091E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.0085379016458575; dx=-5.9869432469701965E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.017953622416932366)=1.0085379016458575; dx=-5.986943347479625E-9 evalInputDelta=0.0
    Armijo: th(0.0538608672507971)=1.0085379016458575; dx=-5.986945632246044E-9 evalInputDelta=0.0
    Armijo: th(0.03590724483386473)=1.0085379016458575; dx=-5.986945639127247E-9 evalInputDelta=0.0
    Armijo: th(0.02693043362539855)=1.0085379016458575; dx=-5.986945677585052E-9 evalInputDelta=0.0
    Armijo: th(0.022442028021165458)=1.0085379016458575; dx=-5.986945725643822E-9 evalInputDelta=0.0
    Armijo: th(0.020197825219048914)=1.0085379016458575; dx=-5.986943375567561E-9 evalInputDelta=0.0
    Armijo: th(0.01907572381799064)=1.0085379016458575; dx=-5.9869433693210286E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0185146731174615)=1.0085379016458575; dx=-5.986943361839318E-9 evalInputDelta=0.0
    Armijo: th(0.018795198467726072)=1.0085379016458575; dx=-5.9869433623099855E-9 evalInputDelta=0.0
    Armijo: th(0.018654935792593787)=1.0085379016458575; dx=-5.986943363496265E-9 evalInputDelta=0.0
    Armijo: th(0.018584804455027644)=1.0085379016458575; dx=-5.986943363372517E-9 evalInputDelta=0.0
    Armijo: th(0.018549738786244573)=1.0085379016458575; dx=-5.986943363372517E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.018532205951853037)=1.0085379016458575; dx=-5.986943361839318E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.018540972369048805)=1.0085379016458575; dx=-5.986943361839318E-9 evalInputDelta=0.0
    Armijo: th(0.018545355577646687)

...skipping 1471 bytes...

    4)=1.0085379016458575; dx=-5.986830997680613E-9 evalInputDelta=0.0
    Armijo: th(0.0998798847622207)=1.0085379016458575; dx=-5.986943325956643E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.08989189628599864)=1.0085379016458575; dx=-5.9869432469701965E-9 evalInputDelta=0.0
    Armijo: th(0.09488589052410967)=1.0085379016458575; dx=-5.986943289838032E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09238889340505416)=1.0085379016458575; dx=-5.986943291775194E-9 evalInputDelta=0.0
    Armijo: th(0.09363739196458191)=1.0085379016458575; dx=-5.9869432956345115E-9 evalInputDelta=0.0
    Armijo: th(0.09301314268481803)=1.0085379016458575; dx=-5.986943295748251E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09270101804493609)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    Armijo: th(0.09285708036487705)=1.0085379016458575; dx=-5.986943278772494E-9 evalInputDelta=0.0
    Armijo: th(0.09277904920490657)=1.0085379016458575; dx=-5.9869432757142976E-9 evalInputDelta=0.0
    Armijo: th(0.09274003362492134)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    Armijo: th(0.09272052583492871)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09271077193993241)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09271564888743056)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09271808736117963)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09271930659805416)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.09271991621649144)=1.0085379016458575; dx=-5.98694327721497E-9 evalInputDelta=0.0
    mu ~= nu (0.09271991621649144): th(0.0)=1.0085379016458575
    Fitness changed from 1.0085379016458575 to 1.0085379016458575
    Static Iteration Total: 20.2349; Orientation: 0.0692; Line Search: 18.8092
    Iteration 2 failed. Error: 1.0085379016458575
    Previous Error: 0.0 -> 1.0085379016458575
    Optimization terminated 2
    Final threshold in iteration 2: 1.0085379016458575 (> -Infinity) after 45.731s (< 3600.000s)
    

Returns:

    1.0085379016458575