Code from BasicOptimizer.scala:88 executed in 1101.46 seconds (9.631 gc):

    val lineSearchInstance: LineSearchStrategy = lineSearchFactory
    val trainer = new IterativeTrainer(trainable)
    trainer.setOrientation(orientation())
    trainer.setMonitor(new TrainingMonitor() {
      override def clear(): Unit = trainingMonitor.clear()
  
      override def log(msg: String): Unit = {
        trainingMonitor.log(msg)
        BasicOptimizer.this.log(msg)
      }
  
      override def onStepFail(currentPoint: Step): Boolean = {
        BasicOptimizer.this.onStepFail(trainable.addRef().asInstanceOf[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.addRef().asInstanceOf[Trainable], currentPoint)
        trainingMonitor.onStepComplete(currentPoint)
        super.onStepComplete(currentPoint)
      }
    })
    trainer.setTimeout(trainingMinutes, TimeUnit.MINUTES)
    trainer.setMaxIterations(trainingIterations)
    trainer.setLineSearchFactory((_: CharSequence) => lineSearchInstance)
    trainer.setTerminateThreshold(java.lang.Double.NEGATIVE_INFINITY)
    val result = trainer.run.asInstanceOf[lang.Double]
    trainer.freeRef()
    result

Logging:

    Reset training subject: 4676505382511
    Reset training subject: 4697876416258
    Adding measurement 10edb8e to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=9.129479430615902;dx=-1.7697391600983627E-7
    Armijo: th(2.154434690031884)=9.129479460418224; dx=-1.7661992085889604E-7 evalInputDelta=-2.9802322387695312E-8
    Armijo: th(1.077217345015942)=9.129479549825191; dx=-1.7786844535922714E-7 evalInputDelta=-1.1920928955078125E-7
    Armijo: th(0.3590724483386473)=9.129479430615902; dx=-1.766196565313518E-7 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=9.129479430615902; dx=-1.77560904974576E-7 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=9.129479430615902; dx=-1.7660724004401383E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.002992270402822061)=9.129479430615902; dx=-1.7754474848411073E-7 evalInputDelta=0.0
    Armijo: th(0.010472946409877214)=9.129479430615902; dx=-1.7785389272638015E-7 evalInputDelta=0.0
    Armijo: th(0.006732608406349637)=9.129479430615902; dx=-1.7789324953623765E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004862439404585849)=9.129479430615902; dx=-1.778741292213775E-7 evalInputDelta=0.0
    Armijo: th(0.005797523905467743)=9.129479430615902; dx=-1.7787552358314668E-7 evalInputDelta=0.0
    Armijo: th(0.005329981655026796)=9.129479430615902; dx=-1.7695390931452562E-7 evalInputDelta=0.0
    Armijo: th(0.005096210529806322)=9.129479430615902; dx=-1.7787712015665137E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.004979324967196086)=9.129479430615902; dx=-1.7789068380048198E-7 evalInputDelta=0.0
    Armijo: th(0.005037767748501204)=9.129479430615902; dx=-1.7789298307889517E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005008546357848645)=9.129479430615902; dx=-1.778692076640367E-7 evalInputDelta=0.0
    Armijo: th(0.005023157053174925)=9.129479430615902; dx=-1.7787762976385895E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.005015851705511784)=9.129479430615902; dx=-1.7787699207209513E-7 evalInputDelta=0.0
    Armijo: th(0.005019504379343355)=9.129479430615902; dx=-1.7660648073830042E-7 evalInputD

...skipping 1603 bytes...

    4005405)=9.129479430615902; dx=-1.7545090048295006E-7 evalInputDelta=0.0
    Armijo: th(0.027031105450045043)=9.129479430615902; dx=-1.7575493031956098E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.02432799490504054)=9.129479430615902; dx=-1.7661379548352256E-7 evalInputDelta=0.0
    Armijo: th(0.025679550177542792)=9.129479430615902; dx=-1.7664236756495068E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.025003772541291665)=9.129479430615902; dx=-1.7547096119494811E-7 evalInputDelta=0.0
    Armijo: th(0.025341661359417227)=9.129479430615902; dx=-1.754694575876907E-7 evalInputDelta=0.0
    Armijo: th(0.025172716950354446)=9.129479430615902; dx=-1.7661252001514826E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.025088244745823055)=9.129479430615902; dx=-1.7663660907692095E-7 evalInputDelta=0.0
    Armijo: th(0.02513048084808875)=9.129479430615902; dx=-1.757452329015295E-7 evalInputDelta=0.0
    Armijo: th(0.0251093627969559)=9.129479430615902; dx=-1.7546466738393777E-7 evalInputDelta=0.0
    Armijo: th(0.025098803771389476)=9.129479430615902; dx=-1.7663640902486534E-7 evalInputDelta=0.0
    Armijo: th(0.025093524258606267)=9.129479430615902; dx=-1.7663132238325244E-7 evalInputDelta=0.0
    Armijo: th(0.02509088450221466)=9.129479430615902; dx=-1.7663204527636215E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.02508956462401886)=9.129479430615902; dx=-1.7659870701993227E-7 evalInputDelta=0.0
    Armijo: th(0.02509022456311676)=9.129479430615902; dx=-1.7663975225529846E-7 evalInputDelta=0.0
    Armijo: th(0.025089894593567807)=9.129479430615902; dx=-1.7662834167756577E-7 evalInputDelta=0.0
    WOLFE (weak): th(0.025089729608793333)=9.129479430615902; dx=-1.7661962916113196E-7 evalInputDelta=0.0
    mu ~= nu (0.025089729608793333): th(0.0)=9.129479430615902
    Fitness changed from 9.129479430615902 to 9.129479430615902
    Static Iteration Total: 498.2005; Orientation: 0.0814; Line Search: 458.8248
    Iteration 2 failed. Error: 9.129479430615902
    Previous Error: 0.0 -> 9.129479430615902
    Optimization terminated 2
    Final threshold in iteration 2: 9.129479430615902 (> -Infinity) after 1101.457s (< 3600.000s)
    

Returns:

    9.129479430615902