Code from BasicOptimizer.scala:75 executed in 35.61 seconds (1.677 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: 1107995922045
    Reset training subject: 1108498753064
    Adding measurement 1bb2e773 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=1.184674683603831;dx=-1.655977699799509E-8
    Armijo: th(2.154434690031884)=1.184674683603831; dx=-1.655977746516482E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=1.184674683603831; dx=-1.655977426592753E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=1.184674683603831; dx=-1.655977783119531E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=1.184674683603831; dx=-1.6559778632581337E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=1.184674683603831; dx=-1.655977698714028E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=1.184674683603831; dx=-1.6559776994052183E-8 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=1.184674683603831; dx=-1.6559776991240156E-8 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=1.184674683603831; dx=-1.6559776994403745E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.004862439404585849)=1.184674683603831; dx=-1.6559776994675394E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.005797523905467743)=1.184674683603831; dx=-1.6559776995515724E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.00626506615590869)=1.184674683603831; dx=-1.6559776995431375E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006498837281129164)=1.184674683603831; dx=-1.6559776994404182E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0066157228437394005)=1.184674683603831; dx=-1.6559776994705563E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006674165625044519)=1.184674683603831; dx=-1.6559776994403745E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.006703387015697078)=1.184674683603831; dx=-1.6559776994403745E-8 evalInputDelta=0.0
    Armijo: th(0.006717997711023358)=1.184674683603831; dx=-1.6559776994403745E-8 evalInputDelta=0.0
    Armijo: th(0.006710692363360218)=1.184674683603831; dx=-1.6559776994403745E-8 evalInputDelta=0.0
    Armijo: th(0.006707039689528647)=1.184674683603831; dx=-1.6559776994403745E-8 evalInputDelta=0.0
    Armijo: th

...skipping 1479 bytes...

    =1.184674683603831; dx=-1.655977697864417E-8 evalInputDelta=0.0
    Armijo: th(0.03611009592296744)=1.184674683603831; dx=-1.65597769809841E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.032499086330670696)=1.184674683603831; dx=-1.6559776981852808E-8 evalInputDelta=0.0
    Armijo: th(0.034304591126819065)=1.184674683603831; dx=-1.6559776981712303E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03340183872874488)=1.184674683603831; dx=-1.655977698389472E-8 evalInputDelta=0.0
    Armijo: th(0.03385321492778197)=1.184674683603831; dx=-1.6559776982000622E-8 evalInputDelta=0.0
    Armijo: th(0.03362752682826342)=1.184674683603831; dx=-1.6559776983572338E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.033514682778504154)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    Armijo: th(0.03357110480338379)=1.184674683603831; dx=-1.6559776983064975E-8 evalInputDelta=0.0
    Armijo: th(0.03354289379094397)=1.184674683603831; dx=-1.6559776983064975E-8 evalInputDelta=0.0
    Armijo: th(0.03352878828472406)=1.184674683603831; dx=-1.6559776982796826E-8 evalInputDelta=0.0
    Armijo: th(0.03352173553161411)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03351820915505913)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03351997234333662)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.033520853937475364)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.03352129473454474)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.033521515133079424)=1.184674683603831; dx=-1.6559776983341174E-8 evalInputDelta=0.0
    mu ~= nu (0.033521515133079424): th(0.0)=1.184674683603831
    Fitness changed from 1.184674683603831 to 1.184674683603831
    Static Iteration Total: 17.3858; Orientation: 0.0687; Line Search: 16.3042
    Iteration 2 failed. Error: 1.184674683603831
    Previous Error: 0.0 -> 1.184674683603831
    Optimization terminated 2
    Final threshold in iteration 2: 1.184674683603831 (> -Infinity) after 35.607s (< 3600.000s)
    

Returns:

    1.184674683603831