Convex optimization control policy

  • Why is convexity desirable in an optimization problem?

    Convexity in gradient descent optimization
    Our goal is to minimize this cost function in order to improve the accuracy of the model.
    MSE is a convex function (it is differentiable twice).
    This means there is no local minimum, but only the global minimum.
    Thus gradient descent would converge to the global minimum..

These types of control policies are tuned by varying the parameters in the optimization problem, such as the LQR weights, to obtain good performance, judged by application- specific metrics. Tuning is often done by hand, or by simple methods such as a crude grid search.
These types of control policies are tuned by varying the parameters in the optimization problem, such as the LQR weights, to obtain good performance, judged by application-specific metrics. Tuning is often done by hand, or by simple methods such as a crude grid search.

What is an example of a convex optimization control policy?

Common examples of such convex optimization control policies (COCPs) include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex control-Lyapunov or approximate dynamic programming (ADP) policies

This article describes Lyapunov optimization for dynamical systems.
It gives an example application to optimal control in queueing networks.

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