Publications related to An optimal first-order primal-dual gap reduction framework for constrained convex optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Baiwei Guo

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Colin Neil Jones, Yuning Jiang, Paul Scharnhorst, Emilio Maddalena

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...

2021

Automated Design: A Journey Across Modelling, Optimization, and Education

Cyril Picard

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...

EPFL2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Bootstrapping massive quantum field theories

Denis Karateev

We propose a new non-perturbative method for studying UV complete unitary quantum field theories (QFTs) with a mass gap in general number of spacetime dimensions. The method relies on unitarity formulated as positive semi-definiteness of the matrix of inne ...

SPRINGER2020

Safe non-smooth black-box optimization with application to policy search

Maryam Kamgarpour, Andreas Krause, Ilnura Usmanova

For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approach based on log barriers to find a local solution of a non-convex non-smooth ...

PMLR2020

Exploiting structure of chance constrained programs via submodularity

Maryam Kamgarpour, Tony Alan Wood

We introduce a novel approach to reduce the computational effort of solving convex chance constrained programs through the scenario approach. Instead of reducing the number of required scenarios, we directly minimize the computational cost of the scenario ...

2019

Allocation of Active Power Reserve from Active Distribution Networks Using a Cost-Benefit Approach: Application to Swissgrid Network

Fabrizio Sossan, Mokhtar Bozorg, Mohsen Kalantar Neyestanaki

The flexibility of distributed energy resources (DERs) accommodated in active distribution networks (ADNs) can be aggregated and then used to provide ancillary services to the transmission system. In this context, this paper presents a linear optimization ...

IEEE2019

Numerical Approximation Of Orthogonal Maps

Marco Picasso, Alexandre Caboussat, Dimitrios Gourzoulidis

Orthogonal maps are the solutions of the mathematical model of paper-folding, also called the origami problem. They consist of a system of first-order fully nonlinear equations involving the gradient of the solution. The Dirichlet problem for orthogonal ma ...

2019

An optimal first-order primal-dual gap reduction framework for constrained convex optimization

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Automated Design: A Journey Across Modelling, Optimization, and Education

A first-order primal-dual method with adaptivity to local smoothness

Bootstrapping massive quantum field theories

Safe non-smooth black-box optimization with application to policy search

Exploiting structure of chance constrained programs via submodularity

Allocation of Active Power Reserve from Active Distribution Networks Using a Cost-Benefit Approach: Application to Swissgrid Network

Numerical Approximation Of Orthogonal Maps

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A first-order primal-dual method with adaptivity to local smoothness

Automated Design: A Journey Across Modelling, Optimization, and Education

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Bootstrapping massive quantum field theories

Exploiting structure of chance constrained programs via submodularity

Numerical Approximation Of Orthogonal Maps

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Allocation of Active Power Reserve from Active Distribution Networks Using a Cost-Benefit Approach: Application to Swissgrid Network

Safe non-smooth black-box optimization with application to policy search