Code from BasicOptimizer.scala:75 executed in 36.14 seconds (0.638 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: 1149341392438
    Reset training subject: 1149865230562
    Adding measurement 2b25595b to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.0869144087967142;dx=-1.6270801628669574E-8
    Armijo: th(2.154434690031884)=1.0869144087967142; dx=-1.6270815709621612E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.0869144087967142; dx=-1.627080667827303E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.0869144087967142; dx=-1.6270811137808806E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.0869144087967142; dx=-1.6270800845509475E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.0869144087967142; dx=-1.6270801647202314E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=1.0869144087967142; dx=-1.6270801638793153E-8 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=1.0869144087967142; dx=-1.6270801648227174E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006732608406349637)=1.0869144087967142; dx=-1.6270801649269743E-8 evalInputDelta=0.0
    Armijo: th(0.008602777408113426)=1.0869144087967142; dx=-1.6270801650591584E-8 evalInputDelta=0.0
    Armijo: th(0.0076676929072315315)=1.0869144087967142; dx=-1.6270801649648022E-8 evalInputDelta=0.0
    Armijo: th(0.007200150656790584)=1.0869144087967142; dx=-1.6270801649563286E-8 evalInputDelta=0.0
    Armijo: th(0.006966379531570111)=1.0869144087967142; dx=-1.6270801649563286E-8 evalInputDelta=0.0
    Armijo: th(0.006849493968959874)=1.0869144087967142; dx=-1.6270801649563286E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006791051187654756)=1.0869144087967142; dx=-1.627080164921569E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006820272578307315)=1.0869144087967142; dx=-1.627080164921569E-8 evalInputDelta=0.0
    Armijo: th(0.006834883273633595)=1.0869144087967142; dx=-1.6270801649563286E-8 evalInputDelta=0.0
    Armijo: th(0.006827577925970454)=1.0869144087967142; dx=-1.6270801649563286E-8 evalInputDelta=0.0
    Armijo: th(0.006823925252138884)=1.0869144087967142; dx=-1.6270801649563286E-8 evalInputDelta=0.0
    WOLFE (weak)

...skipping 1507 bytes...

    522)=1.0869144087967142; dx=-1.6270801638497363E-8 evalInputDelta=0.0
    Armijo: th(0.03675133291080435)=1.0869144087967142; dx=-1.627080164352896E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03307619961972392)=1.0869144087967142; dx=-1.627080164633542E-8 evalInputDelta=0.0
    Armijo: th(0.034913766265264136)=1.0869144087967142; dx=-1.6270801641237525E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03399498294249403)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.03445437460387908)=1.0869144087967142; dx=-1.627080164205163E-8 evalInputDelta=0.0
    Armijo: th(0.03422467877318655)=1.0869144087967142; dx=-1.6270801642824527E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03410983085784029)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.03416725481551342)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.03413854283667686)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.03412418684725858)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03411700885254944)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.03412059784990401)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.034118803351226724)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.034117906101888085)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.03411745747721876)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    Armijo: th(0.0341172331648841)=1.0869144087967142; dx=-1.6270801642541036E-8 evalInputDelta=0.0
    mu ~= nu (0.03411700885254944): th(0.0)=1.0869144087967142
    Fitness changed from 1.0869144087967142 to 1.0869144087967142
    Static Iteration Total: 16.4747; Orientation: 0.0662; Line Search: 15.3529
    Iteration 2 failed. Error: 1.0869144087967142
    Previous Error: 0.0 -> 1.0869144087967142
    Optimization terminated 2
    Final threshold in iteration 2: 1.0869144087967142 (> -Infinity) after 36.137s (< 3600.000s)
    

Returns:

    1.0869144087967142