Publications related to Interchangeability with thresholds and degradation factors for Soft CSPs

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

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

Learning Generalized Nash Equilibria in a Class of Convex Games

Maryam Kamgarpour

We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel dis ...

2019

A Single-Phase, Proximal Path-Following Framework

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose a new proximal path-following framework for a class of constrained convex problems. We consider settings where the nonlinear-and possibly nonsmooth-objective part is endowed with a proximity operator, and the constraint set is equipped with a se ...

INFORMS2018

Memetic Viability Evolution for Constrained Optimization

Dario Floreano, Andrea Maesani, Giovanni Iacca

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrai ...

Institute of Electrical and Electronics Engineers2016

Modelling Resource Dependencies

Simon Bliudze, Alena Simalatsar, Alina Zolotukhina

The major research in the resource management literature focuses primarily on two directions: 1) specification languages for formulating resource requests and 2) constraint problems modelling allocation and scheduling. Both directions assume the knowledge ...

2016

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Revisiting Taxonomy Induction over Wikipedia

Amit Gupta

Guided by multiple heuristics, a unified taxonomy of entities and categories is distilled from the Wikipedia category network. A comprehensive evaluation, based on the analysis of upward generalization paths, demonstrates that the taxonomy supports general ...

2016

Protecting Privacy through Distributed Computation in Multi-agent Decision Making

Boi Faltings, Thomas Léauté

As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed com ...

AI Access Foundation2013

New Bounds for Random Constraint Satisfaction Problems via Spatial Coupling

Rüdiger Urbanke, Nicolas Macris, Seyed Hamed Hassani

This paper is about a novel technique called spatial coupling and its application in the analysis of random constraint satisfaction problems (CSP). Spatial Coupling was recently invented in the area of error correcting codes where it has resulted in effici ...

2013

Interchangeability with thresholds and degradation factors for Soft CSPs

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

Learning Generalized Nash Equilibria in a Class of Convex Games

A Single-Phase, Proximal Path-Following Framework

Memetic Viability Evolution for Constrained Optimization

Modelling Resource Dependencies

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Revisiting Taxonomy Induction over Wikipedia

Protecting Privacy through Distributed Computation in Multi-agent Decision Making

New Bounds for Random Constraint Satisfaction Problems via Spatial Coupling

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

A Single-Phase, Proximal Path-Following Framework

Learning Generalized Nash Equilibria in a Class of Convex Games

Memetic Viability Evolution for Constrained Optimization

Modelling Resource Dependencies

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Revisiting Taxonomy Induction over Wikipedia

Protecting Privacy through Distributed Computation in Multi-agent Decision Making

New Bounds for Random Constraint Satisfaction Problems via Spatial Coupling