no code implementations • 10 Nov 2023 • Amir Ali Ahmadi, Abraar Chaudhry, Jeffrey Zhang
At each step, our $d^{\text{th}}$-order method uses semidefinite programming to construct and minimize a sum of squares-convex approximation to the $d^{\text{th}}$-order Taylor expansion of the function we wish to minimize.
no code implementations • 20 May 2023 • Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
For $T=2$, we give a semidefinite representation of the set of safe initial conditions and show that $\lceil n/2 \rceil$ trajectories generically suffice for safe learning.
no code implementations • 24 Nov 2020 • Amir Ali Ahmadi, Abraar Chaudhry, Vikas Sindhwani, Stephen Tu
For our first two results, we consider the setting of safely learning linear dynamics.
