Here are the slides from Lectures

11_Merton Portfolio Optimization

Please read Section 2.4 of the notes

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## Merton Portfolio Optimization

## Diffusion Control

## Stochastic Integration (a quick intro)

## Continuous Time Dynamic Programming

## LQR and Kalman Filter

## ODE method for Stochastic Approximation

## Lyapunov Functions

Here are the slides from Lectures

11_Merton Portfolio Optimization

Please read Section 2.4 of the notes

Here are the slides from Lectures

Please read Section 2.3 of the notes

Here are the slides from Lectures

Please read Section 2.2 of the notes

Here are the slides from Lectures

8_Continuous Time Dynamic Programming

Please read Section 2.1 of the notes

Here are the slides from Lectures

Please read these notes [which will be later added to the main set of notes]:

We consider the Robbins-Monro update

and argue that this can be approximated by the o.d.e.:

Lyapunov functions are an extremely convenient device for proving that a dynamical system converges. We cover:

- The Lyapunov argument
- La Salle’s Invariance Principle
- An Alternative argument for Convex Functions
- Exponential Convergence Rates