Calculus and linear algebra practice for quant interviews
Taylor bounds, Newton’s method, eigenvectors, PSD matrices and row reduction, timed and scored.
DIRECTIONS
What the Gradient Lab games train
Eigenvector Spotter
Given a 2x2 matrix and four candidate directions, click the one that A merely stretches instead of rotating - the eigenvector. Read the Eigenvector Spotter guide.
Lagrange Optimizer
Maximize xy on a budget line ax + by = k by solving the Lagrange condition ∇f = λ∇g for x* with nothing but the algebra - there is no live readout to chase. Read the Lagrange Optimizer guide.
Newton Stepper
Predict how many Newton iterations it takes to drive x² - a = 0 from x₀ = 1 to within 1e-4 of √a - then watch the step table and see how quadratic convergence behaves. Read the Newton Stepper guide.
PSD Classifier
Classify a symmetric 2x2 matrix as positive definite, positive semidefinite, indefinite, or negative definite using trace and determinant - the test that decides whether it could be a covariance matrix. Read the PSD Classifier guide.
Taylor Slider
Sum a Taylor polynomial by hand - order 2, 3, or 4 for eˣ, sin, cos, or ln(1+x) at a given point - and see how fast the error shrinks. Read the Taylor Slider guide.