Learning convex optimization control policies

  • What is convex Optimisation in machine learning?

    Convex optimization can be used to optimize algorithms by improving the speed at which they converge to a solution.
    Additionally, it can be used to solve linear systems of equations by finding the best approximation to the system, rather than computing an exact answer..

  • What is the role of convex optimization in machine learning and how can it be used to improve the performance of a model?

    Convex optimization algorithms are used to solve issues with convex objective functions.
    These methods are computationally efficient and can find the global optimum.
    Gradient descent and Newton's technique are two examples of convex optimization algorithms..

  • Why do we use convex optimization in machine learning?

    Convex optimization has become an essential tool in machine learning because many real-world problems can be modeled as convex optimization problems.
    For example, in classification problems, the goal is to find the best hyperplane that separates the data points into different classes..

What are learning Convex optimization control policies?

Learning Convex Optimization Control Policies Akshay Agrawal Shane Barratt Stephen Boyd Bartolomeo Stellato March 16, 2020 Abstract Many control policies used in various applications determine the input or action by solving a convex optimization problem that depends on the current state and some parameters

What is an example of embedded (convex) optimization?

A good example of embedded (convex) optimization is model predictive control, an automatic control technique that requires the solution of a (convex) quadratic program at each step

Model predictive control is now widely used in the chemical process control industry; see Morari and Zaļ¬rou [MZ89]

1 Introduction

  • 1.1 Convex optimization control policies We consider the control of a stochastic dynamical system with known dynamics, using a control policy that determines the input or action by solving a convex optimization problem. We call such policies ...
  • 1.2 Related work Dynamic programming. The Markov decision process (MDP) is a general stochastic control problem that can be solved in principle ...
  • 1.3 Outline

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