4 papers
Track-centric Iterative Learning for Global Trajectory Optimization in Autonomous Racing
Youngim Nam, Jungbin Kim, Kyungtae Kang +1
This paper presents a global trajectory optimization framework for minimizing lap time in autonomous racing under uncertain vehicle dynamics. Optimizing the trajectory over the ful…
Horospherically Convex Optimization on Hadamard Manifolds Part I: Analysis and Algorithms
Christopher Criscitiello, Jungbin Kim
Geodesic convexity (g-convexity) is a natural generalization of convexity to Riemannian manifolds. However, g-convexity lacks many desirable properties satisfied by Euclidean conve…
A Proof of Exact Convergence Rate of Gradient Descent. Part I. Performance Criterion
Jungbin Kim
We prove the exact worst-case convergence rate of gradient descent for smooth strongly convex optimization, with respect to the performance criterion $\Vert \nabla f(x_N)\Vert^2/(f…
A Proof of the Exact Convergence Rate of Gradient Descent
Jungbin Kim
We prove the exact worst-case convergence rate of gradient descent for smooth strongly convex optimization on . Concretely, assuming that the objective function i…