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Optimality — Hamilton-Jacobi-Bellman (HJB) versus Riccati Most of the literature on optimal control discuss Hamilton-Jacobi-Bellman (HJB) equations for optimality In dynamics however, Riccati equations are used instead Jacobi Bellman equations are also
mathematical economics - Solving the Hamilton-Jacobi-Bellman equation . . . Actually, I just want to make sure, that if I've solved the HJB and recoverd the associated state and control trajectories, that I don't have to be concerned with any additional optimality conditions Solution I Attempt I think I was able to derive necessary conditions from the maximum principle by the HJB equation itself
Mean Field Games Solving the Couple HJB system The mean field game for a given minimization problem leads to a coupled system of HJB and Fokker-Planck equations The HJB is forward in time, where as the FPK equations are backwards How does one
Hamilton-Jacobi-Bellman equation for Levy processes However I don't understand how the last term in the HJB equation is connected to the last term in (2) Especially the $\nu$ is confusing me $\nu$ is the Levy measure i guess
Role of verification theorems in stochastic optimal control? On deterministic versus stochastic: The above verification HJB classical solution issue is for both the deterministic and the stochastic case For example, see Ch 4, Sec 2 of 1, which specifically talks about verification theorems for the first order HJB PDEs in deterministic optimal control
stochastic calculus - Is it possible to derive, if informally, the . . . It satisfies the Feynman-Kac equation $$ \mathcal {A} V (t, x)+ C (t, x, u (t,x)) = 0$$ From this, Feynman-Kac is a special case of HJB, when you have no control Is it possible to go the other way, i e give a derivation of HJB from Feynman-Kac?