Gradient Descent Quant Interview Guide
Gradient descent quant interview guide for objectives, gradients, step sizes, convergence, scaling, stochastic variants, and caveats.
Candidates explaining optimization in machine learning, calibration, and numerical models.
Gradient descent follows local slope
Gradient descent updates parameters in the direction that reduces the objective according to the gradient. The method is simple but sensitive to scaling and step size.
Learning rate controls movement
A step that is too large can diverge or oscillate, while a step that is too small can converge slowly. Schedules and adaptive methods address this tradeoff.
Concrete example
For linear regression with squared loss, gradient descent updates coefficients based on prediction errors and feature values instead of solving directly.
Scaling affects convergence
Features with very different scales can make the objective poorly conditioned. Standardization or preconditioning can make optimization easier.
Common mistakes
Candidates often imply gradient descent always finds the best solution. Strong answers discuss convexity, local minima, initialization, and stopping criteria.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.