Titlebook: Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems; Xi-Ren Cao Book 2020 Springer Nature Switzerland AG 2020

閑蕩

Optimal Control of Diffusion Processes, The value function for finite-horizon problems, or the potential function for long-run average, can be smooth or semi-smooth (both one-sided first-order derivatives exist, but not equal). Explicit optimality conditions are derived at both smooth and semi-smooth points. This extends the famous Hamil

積習難改

Degenerate Diffusion Processes,ization of multi-class degenerate diffusion processes. We also show that under some conditions, the performance function of finite-horizon optimization problems, or the potential function of the long-run average optimization problems, is semi-smooth at degenerate points and smooth at non-degenerate

Common-Migraine

乞討

Performance-Derivative-Based Optimization,itesimally close, the performance-difference formula becomes the performance-derivative formula. The performance-derivative-based approach is more suitable for non-linear or non-additive performance measure. In this chapter, we derive the first-order optimality conditions (or the differential versio

指派

Degenerate Diffusion Processes,at these degenerate points in the Ito-Tanaka formula. With these special features in consideration, various optimization problems such as long-run average, finite-horizon, optimal stopping, and singular control, become simpler.

Fortify

0178-5354 chastic systems, promoting new research pathways.Shows the r.This monograph applies the relative optimization approach to time nonhomogeneous continuous-time and continuous-state dynamic systems. The approach is intuitively clear and does not require deep knowledge of the mathematics of partial diff

Detoxification

希望

錯誤

Multi-dimensional Diffusion Processes,d stochastic calculus of multi-dimensional systems, in particular, for problems with non-smooth features and degenerate points. The analysis is intuitive and results are preliminary, and hopefully they would motivate new research topics.

Haphazard