Publications related to Temperature Control of High Performance Multicore Platforms Using 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

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Giancarlo Ferrari Trecate, Maryam Kamgarpour, Yuning Jiang, Baiwei Guo

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

2023

Development of a large-area, light-weight module using the MALTA monolithic pixel detector

Edoardo Charbon, Francesco Piro, Ashish Sharma

The MALTA pixel chip is a 2 cm x 2 cm large monolithic pixel detector developed in the Tower 180 nm imaging process. The chip contains four CMOS transceiver blocks at its sides which allow chip-to-chip data transfer. The power pads are located mainly at th ...

ELSEVIER2022

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

Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Mohamed Mostafa Sabry Aly, Halima Najibi

Deeply-scaled three-dimensional (3D) Multi-Processor Systems-on-Chip (MPSoCs) enable high performance and massive communication bandwidth for next-generation computing. However, as process nodes shrink, temperature-dependent leakage dramatically increases, ...

2020

Implications of ANEC for SCFTs in four dimensions

Andrea Manenti, Alessandro Vichi

We explore consequences of the Averaged Null Energy Condition (ANEC) for scaling dimensions Delta of operators in four-dimensional N = 1 superconformal field theories. We show that in many cases the ANEC bounds are stronger than the corresponding unitarity ...

SPRINGER2020

Stochastic Frank-Wolfe for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever

A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed fo ...

2019

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H-infinity performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controller ...

IEEE2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the p ...

Elsevier2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H ∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the ...

2018

Temperature Control of High Performance Multicore Platforms Using Convex Optimization

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Development of a large-area, light-weight module using the MALTA monolithic pixel detector

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

Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology

Implications of ANEC for SCFTs in four dimensions

Stochastic Frank-Wolfe for Composite Convex Minimization

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

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

Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology

Implications of ANEC for SCFTs in four dimensions

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Stochastic Frank-Wolfe for Composite Convex Minimization

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Development of a large-area, light-weight module using the MALTA monolithic pixel detector

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance