Class lsst::meas::modelfit::OptimizerControl

class OptimizerControl

Configuration object for Optimizer.

Many of these configuration options pertain to how the trust region is updated. It’s easiest to understand these together rather than separately. At each iteration, a quadratic model of the objective function is formed. We can use this model to predict how we expect the objective function to behave over a step, and compare it to how the actual objective function behaves. To do this, we’ll use the ratio of the actual reduction in the objective function to the predicted reduction in the objective function, and call this \(\rho\). Then,

• the step is accepted, and the parameters updated, when \(\rho >\) stepAcceptThreshold.

• if \(\rho > \) trustRegionGrowReductionRatio and the length of the step is greater than trustRegionGrowStepFraction times the current trust region radius, the trust region radius will be multiplied by trustRegionGrowFactor.

• if trustRegionShrinkMinReductionRatio \(< \rho < \) trustRegionShrinkMaxReductionRatio, the trust region radius will be multiplied by trustRegionShrinkFactor.