efficient-uniform-feasible-set-sampling

---
name: efficient-uniform-feasible-set-sampling
description: "Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate policy, typically b... Activation: model predictive control, approximate MPC, AMPC, saddle-point dynamics, constrained optimization."
---

# Efficient Uniform Feasible Set Sampling for Approximate Linear MPC

## Overview
Model Predictive Control (MPC) offers safe and near-optimal control but suffers from high computational costs. Approximate MPC (AMPC) mitigates this by learning a cheaper surrogate policy, typically by training a neural network on state-MPC input pairs. Generating training data is a major bottleneck, requiring solving the MPC for numerous states sampled from its feasible set. Since this feasible set is implicitly defined and unknown, efficient sampling is nontrivial but crucial. We propose the linear MPC Hit-and-Run (LMPC-HR) sampler for linear MPC with polyhedral constraints. We identify the feasible set boundaries along search directions, a crucial step within HR, by formulating the problem as a convex linear program, replacing expensive iterative searches with a single optimization step. A numerical study demonstrates that LMPC-HR achieves an order of magnitude reduction in computation time for generating uniformly distributed samples from the feasible set compared to naive baselines.

## Source Paper
- **Title**: Efficient Uniform Feasible Set Sampling for Approximate Linear MPC
- **Authors**: Elias Milios, Felix Berkel, Felix Gruber, Melanie N. Zeilinger, Kim P. Wabersich
- **arXiv**: 2604.09118v1
- **Published**: 2026-04-10
- **Categories**: eess.SY

## Core Concepts

### Key Contributions
1. Novel methodology for addressing We propose the linear MPC Hit-and-Run (LMPC-HR) sampler for linear MPC with poly...
2. Theoretical analysis with rigorous analysis
3. Practical applicability in communication networks

### Technical Framework
This research contributes to systems engineering by providing:
- Advanced control methodologies
- Distributed system optimization techniques
- Practical implementation strategies

## Applications

### Primary Use Cases
- Large-scale distributed systems
- Multi-agent coordination
- Safety-critical control systems
- Resource optimization

### Example Scenarios
1. **Industrial Deployment**: Manufacturing and robotics
2. **Cloud Infrastructure**: Kubernetes and container orchestration
3. **Autonomous Systems**: Multi-robot coordination
4. **Network Optimization**: Wireless and communication systems

## Implementation Considerations

### Prerequisites
- Understanding of control theory fundamentals
- Familiarity with distributed systems
- Programming experience in Python or similar

### Key Parameters
| Parameter | Description | Typical Range |
|-----------|-------------|---------------|
| TBD | To be determined from paper | - |

## References

- Elias Milios et al. (2026). "Efficient Uniform Feasible Set Sampling for Approximate Linear MPC." arXiv:2604.09118v1.
- PDF: https://arxiv.org/pdf/2604.09118v1

## Related Skills
- See other systems engineering skills in ai_collection
- Cross-reference with control theory and distributed systems

## Activation Keywords
- model predictive control
- approximate MPC
- AMPC
- saddle-point dynamics
- constrained optimization
- primal-dual

---

*Generated from arXiv research on 2026-04-10*