Concept

Interior-point method

Related publications (197)

Interior Point Decomposition for Multi-Agent Optimization

Colin Neil Jones, Altug Bitlislioglu, Ivan Pejcic

In this paper we present the application of the interior-point decomposition (IPD) method, which was originally formulated for stochastic programming, to optimization problems involving multiple agents that are coupled through constraints and objectives. I ...

2017

Rapport with Virtual Agents: What do Human Social Cues and Personality Explain?

Daniel Gatica-Perez, Oya Aran

Rapport has been recognized as an important aspect of relationship building. While rapport in the context of human-human interaction has been widely studied, how it can be established and maintained in human-agent interaction has been studied only recently ...

2017

Complexity Certification of the Fast Alternating Minimization Algorithm for Linear MPC

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

In this technical note, the fast alternating minimization algorithm (FAMA) is proposed to solve model predictive control (MPC) problems with polytopic and second-order cone constraints. Two splitting strategies with efficient implementations for MPC proble ...

2017

Decentralized and distributed robust control invariance for constrained linear systems

Giancarlo Ferrari Trecate

In this paper we propose an efficient procedure to compute time-varying Robust Control Invariant (RCI) sets for large-scale systems arising from the interconnection of M Linear Time-Invariant (LTI) constrained subsystems. In particular, the associated stat ...

IEEE2017

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Colin Neil Jones, Milan Korda, Georgios Stathopoulos

This work presents an algorithmic scheme for solving the infinite-time constrained linear quadratic regulation problem. We employ an accelerated version of a popular proximal gradient scheme, commonly known as the Forward-Backward Splitting (FBS), and prov ...

Institute of Electrical and Electronics Engineers2017

Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds

Jonas Buchli, Farbod Farshidian, Alexander Winkler

In this letter, we present a trajectory optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In c ...

2017

How to learn a graph from smooth signals

Vasilis Kalofolias

We propose a framework that learns the graph structure underlying a set of smooth signals. Given a real m by n matrix X whose rows reside on the vertices of an unknown graph, we learn the edge weights w under the smoothness assumption that trace(X^TLX) is ...

2016

Scampi: a robust approximate message-passing framework for compressive imaging

Florent Gérard Krzakala, Jean François Emmanuel Barbier

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

A Parametric Nonconvex Decomposition Algorithm for Real-Time and Distributed NMPC

Colin Neil Jones, Jean-Hubert Hours

A novel decomposition scheme to solve parametric nonconvex programs as they arise in Nonlinear Model Predictive Control (NMPC) is presented. It consists of a fixed number of alternating proximal gradient steps and a dual update per time step. Hence, the pr ...

2016

Performance limits of single-agent and multi-agent sub-gradient stochastic learning

Ali H. Sayed, Bicheng Ying

This work examines the performance of stochastic sub-gradient learning strategies, for both cases of stand-alone and networked agents, under weaker conditions than usually considered in the literature. It is shown that these conditions are automatically sa ...

IEEE2016

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