Code from BasicOptimizer.scala:75 executed in 190.39 seconds (2.024 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: 912776598741600
    Reset training subject: 912780048857300
    Adding measurement 75cea0b to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.6011758148670197;dx=-2.3164871698948686E-8
    Armijo: th(2.154434690031884)=0.6011758148670197; dx=-2.3100268040914207E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.6011758148670197; dx=-2.3100267821291974E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0017098688016126062)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0023510696022173336)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.0026716700025196972)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.0025113698023685157)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.0024312197022929244)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002391144652255129)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.0024111821772740266)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.0024011634147645777)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002396154033509853)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.002398658724137215)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.002397406378823534)=0.6011758148670197; dx=-2.310026779

...skipping 1634 bytes...

    ta=0.0
    Armijo: th(0.012907000072148102)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.011616300064933291)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.012261650068540696)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.011938975066736994)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.012100312567638844)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.012019643817187918)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.011979309441962457)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.011999476629575187)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.011989393035768823)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.01198435123886564)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    Armijo: th(0.011981830340414048)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.011980569891188252)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.01198120011580115)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0119815152281076)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.011981672784260825)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.011981751562337436)=0.6011758148670197; dx=-2.3100267795471012E-8 evalInputDelta=0.0
    mu ~= nu (0.011981751562337436): th(0.0)=0.6011758148670197
    Fitness changed from 0.6011758148670197 to 0.6011758148670197
    Static Iteration Total: 87.7064; Orientation: 0.0692; Line Search: 80.7944
    Iteration 2 failed. Error: 0.6011758148670197
    Previous Error: 0.0 -> 0.6011758148670197
    Optimization terminated 2
    Final threshold in iteration 2: 0.6011758148670197 (> -Infinity) after 190.386s (< 1800.000s)
    

Returns:

    0.6011758148670197