# Overcoming the curse of dimensionality in dynamic programming by tensor decompositions

Lead Research Organisation:
University of Bath

Department Name: Mathematical Sciences

### Abstract

Almost 60 years ago Richard Bellman coined the expression ``curse of dimensionality'' when referring to the overwhelming computational complexity associated with the solution of multi-stage decision processes through dynamic programming (DP), leading to the well-known Bellman equation.

Nowadays the curse of dimensionality has become an ubiquitous expression shared in different fields such as numerical analysis, compressed sensing and machine learning. However, it is in the computation of optimal feedback laws for the control of dynamical systems where its meaning continues to be most evident.

Consider a simple pendulum, which is characterised by two variables, the position of the mass, and its velocity.

A classical demonstration is the stabilisation of the pendulum in the unstable upwards position by moving the base adaptively.

To synthesize the control signal for the actuator of the base that would be robust to stochastic fluctuations (e.g. from the air),

one needs to compute a feedback map as a two-dimensional function of the position and velocity.

This feedback map satisfies the two-dimensional partial differential equation (PDE), which has no analytic solution in general.

Solving it numerically requires a discretisation of both the position and velocity with some n points.

However, the total number of unknowns in the feedback map is n^2, corresponding to all combinations of the position and velocity points,

and therefore the computations take longer.

For d variables, the complexity of the straightforward numerical solutions grows exponentially as n^d.

Even a simple quadrocopter model is described by 12 variables, and modern applications in particle physics or opinion dynamics require optimal control of dynamical systems, which are in turn PDEs, discretised with hundreds or thousands of variables.

However, such systems are often very special in the sense that each variable is driven effectively by only neighbouring variables in the dynamics.

In this case, the sought feedback map admits a tensor approximation with (almost) separated variables.

This allows us to reduce the computational complexity dramatically, to a number of operations growing only polynomially with the number of variables, albeit at a price of introducing some error.

This error can be further corrected by using methods from reinforcement learning, similar to those that made the winning artificial Go player.

Nowadays the curse of dimensionality has become an ubiquitous expression shared in different fields such as numerical analysis, compressed sensing and machine learning. However, it is in the computation of optimal feedback laws for the control of dynamical systems where its meaning continues to be most evident.

Consider a simple pendulum, which is characterised by two variables, the position of the mass, and its velocity.

A classical demonstration is the stabilisation of the pendulum in the unstable upwards position by moving the base adaptively.

To synthesize the control signal for the actuator of the base that would be robust to stochastic fluctuations (e.g. from the air),

one needs to compute a feedback map as a two-dimensional function of the position and velocity.

This feedback map satisfies the two-dimensional partial differential equation (PDE), which has no analytic solution in general.

Solving it numerically requires a discretisation of both the position and velocity with some n points.

However, the total number of unknowns in the feedback map is n^2, corresponding to all combinations of the position and velocity points,

and therefore the computations take longer.

For d variables, the complexity of the straightforward numerical solutions grows exponentially as n^d.

Even a simple quadrocopter model is described by 12 variables, and modern applications in particle physics or opinion dynamics require optimal control of dynamical systems, which are in turn PDEs, discretised with hundreds or thousands of variables.

However, such systems are often very special in the sense that each variable is driven effectively by only neighbouring variables in the dynamics.

In this case, the sought feedback map admits a tensor approximation with (almost) separated variables.

This allows us to reduce the computational complexity dramatically, to a number of operations growing only polynomially with the number of variables, albeit at a price of introducing some error.

This error can be further corrected by using methods from reinforcement learning, similar to those that made the winning artificial Go player.

### Organisations

### Publications

Wells C
(2023)

*Minimising emissions from flights through realistic wind fields with varying aircraft weights*in Transportation Research Part D: Transport and Environment
Wells C
(2022)

*The role of airspeed variability in fixed-time, fuel-optimal aircraft trajectory planning*in Optimization and Engineering
Kalise D
(2023)

*Consensus-based optimization via jump-diffusion stochastic differential equations*in Mathematical Models and Methods in Applied Sciences
Dolgov S
(2023)

*Data-Driven Tensor Train Gradient Cross Approximation for Hamilton-Jacobi-Bellman Equations*in SIAM Journal on Scientific Computing
Dolgov S
(2021)

*Tensor Decomposition Methods for High-dimensional Hamilton--Jacobi--Bellman Equations*in SIAM Journal on Scientific Computing
Dolgov S
(2022)

*Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control*in IFAC-PapersOnLine
Antil H
(2022)

*TTRISK: Tensor train decomposition algorithm for risk averse optimization*in Numerical Linear Algebra with Applications
Alla A
(2023)

*State-dependent Riccati equation feedback stabilization for nonlinear PDEs*in Advances in Computational MathematicsDescription | Computational algorithms are developed to synthesize feedback control signal for large-scale dynamical systems, including consensus stabilization in the interacting particle swarm and vorticity minimisation in fluid dynamics. |

Exploitation Route | The control function precomputed by the developed algorithms allows fast evaluation of the control signal at the given state of the system. This can be programmed into a low-performance hardware controlling e.g. fluid flows in real time. |

Sectors | Digital/Communication/Information Technologies (including Software),Electronics |

URL | https://github.com/saluzzi/TT-Gradient-Cross |

Title | TT-Gradient-Cross |

Description | Tensor Train (TT) Gradient Cross algorithm for the resolution of Hamilton Jacobi Bellman equation. |

Type Of Technology | Software |

Year Produced | 2022 |

Open Source License? | Yes |

Impact | none so far |

URL | https://github.com/saluzzi/TT-Gradient-Cross |

Title | TT-HJB |

Description | Matlab package to form and solve Hamilton-Jacobi-Bellman (HJB) equations for the optimal feedback control synthesis for stochastic dynamical systems. |

Type Of Technology | Software |

Year Produced | 2020 |

Open Source License? | Yes |

Impact | No **notable** impacts traced yet, but we have received a few requests from PhD students working on similar problems who would like to build on our code. |

URL | https://github.com/dolgov/TT-HJB |