Code from BasicOptimizer.scala:75 executed in 69.25 seconds (1.240 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: 909777227322000
    Reset training subject: 909778947901000
    Adding measurement 1bc4ce09 to history. Total: 0
    LBFGS Accumulation History: 1 points
    Constructing line search parameters: GD+Trust
    th(0)=0.6792539656162262;dx=-3.453673866151328E-8
    Armijo: th(2.154434690031884)=0.6792539656162262; dx=-3.4536738801851114E-8 evalInputDelta=0.0
    Armijo: th(1.077217345015942)=0.6792539656162262; dx=-3.453673863251609E-8 evalInputDelta=0.0
    Armijo: th(0.3590724483386473)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.08976811208466183)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.017953622416932366)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.002992270402822061)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(4.2746720040315154E-4)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.0017098688016126062)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001068668001007879)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0013892684013102426)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015495686014614244)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.0016297187015370152)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015896436514992198)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.0016096811765181174)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0015996624140086685)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.001604671795263393)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.0016071764858907552)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.0016084288312044363)=0.6792539656162262; dx=-3.45367

...skipping 1712 bytes...

    28E-8 evalInputDelta=0.0
    Armijo: th(0.008657103347597193)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.007791393012837475)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.008224248180217334)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008007820596527404)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.00811603438837237)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.008061927492449886)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008034874044488646)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.008048400768469266)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.008041637406478955)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.0080382557254838)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    Armijo: th(0.008036564884986223)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008035719464737434)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.00803614217486183)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008036353529924026)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008036459207455125)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    WOLFE (weak): th(0.008036512046220674)=0.6792539656162262; dx=-3.453673866151328E-8 evalInputDelta=0.0
    mu ~= nu (0.008036512046220674): th(0.0)=0.6792539656162262
    Fitness changed from 0.6792539656162262 to 0.6792539656162262
    Static Iteration Total: 31.2473; Orientation: 0.0146; Line Search: 28.8634
    Iteration 2 failed. Error: 0.6792539656162262
    Previous Error: 0.0 -> 0.6792539656162262
    Optimization terminated 2
    Final threshold in iteration 2: 0.6792539656162262 (> -Infinity) after 69.252s (< 720.000s)
    

Returns:

    0.6792539656162262