Code from BasicOptimizer.scala:75 executed in 58.52 seconds (2.085 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: 764608341386
    Reset training subject: 765545529607
    Adding measurement 9dd9418 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.7142306566238403;dx=-5.892109001895919E-9
    Armijo: th(2.154434690031884)=0.7142306566238403; dx=-5.892057067512097E-9 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.7142306566238403; dx=-5.892057603576474E-9 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.7142306566238403; dx=-5.8920567820609885E-9 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.7142306566238403; dx=-5.892108995604723E-9 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.7142306566238403; dx=-5.8921090003502505E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=0.7142306566238403; dx=-5.892109001385251E-9 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=0.7142306566238403; dx=-5.892109000661031E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.006732608406349637)=0.7142306566238403; dx=-5.8921090013862496E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.008602777408113426)=0.7142306566238403; dx=-5.892109001158948E-9 evalInputDelta=0.0
    Armijo: th(0.00953786190899532)=0.7142306566238403; dx=-5.892109000788459E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009070319658554373)=0.7142306566238403; dx=-5.892109000811761E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009304090783774846)=0.7142306566238403; dx=-5.89210900079882E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.009420976346385083)=0.7142306566238403; dx=-5.89210900079882E-9 evalInputDelta=0.0
    Armijo: th(0.009479419127690202)=0.7142306566238403; dx=-5.892109000788459E-9 evalInputDelta=0.0
    Armijo: th(0.009450197737037643)=0.7142306566238403; dx=-5.89210900079882E-9 evalInputDelta=0.0
    Armijo: th(0.009435587041711362)=0.7142306566238403; dx=-5.89210900079882E-9 evalInputDelta=0.0
    Armijo: th(0.009428281694048222)=0.7142306566238403; dx=-5.89210900079882E-9 evalInputDelta=0.0
    Armijo: th(0.009424629020216652)=0.7142306566238403; dx=-5.89210900079882E-9 evalInputDelta=0.0
    Armijo: th(0.00942280268

...skipping 1479 bytes...

    566238403; dx=-5.892108996955493E-9 evalInputDelta=0.0
    Armijo: th(0.05074388633810456)=0.7142306566238403; dx=-5.892108998631302E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.0456694977042941)=0.7142306566238403; dx=-5.892108998812909E-9 evalInputDelta=0.0
    Armijo: th(0.04820669202119933)=0.7142306566238403; dx=-5.8921089985807425E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.046938094862746714)=0.7142306566238403; dx=-5.892108998472604E-9 evalInputDelta=0.0
    Armijo: th(0.04757239344197302)=0.7142306566238403; dx=-5.892108998482043E-9 evalInputDelta=0.0
    Armijo: th(0.04725524415235986)=0.7142306566238403; dx=-5.892108998482043E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.047096669507553285)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    Armijo: th(0.047175956829956574)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    Armijo: th(0.047136313168754926)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    Armijo: th(0.047116491338154105)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    Armijo: th(0.04710658042285369)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.04710162496520349)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.04710410269402859)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.04710534155844114)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.047105960990647414)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    WOLFE (weak): th(0.04710627070675055)=0.7142306566238403; dx=-5.892108998473837E-9 evalInputDelta=0.0
    mu ~= nu (0.04710627070675055): th(0.0)=0.7142306566238403
    Fitness changed from 0.7142306566238403 to 0.7142306566238403
    Static Iteration Total: 26.4322; Orientation: 0.0629; Line Search: 24.5259
    Iteration 2 failed. Error: 0.7142306566238403
    Previous Error: 0.0 -> 0.7142306566238403
    Optimization terminated 2
    Final threshold in iteration 2: 0.7142306566238403 (> -Infinity) after 58.520s (< 3600.000s)
    

Returns:

    0.7142306566238403