Publications associées à Relaxation continue

An adaptive primal-dual framework for nonsmooth convex minimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new self-adaptive and double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov’s smoothing technique via general Bregman distance functions. It self-adaptiv ...

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Retrofitting, district heating and energy storage: neighborhood energy planning

Jérôme Henri Kämpf, Jakob Moritz Fabian Rager, Fabien Luc Poumadère, Diane Von Gunten

We compare simulated energy retrofitting of individual buildings with energy refurbishments optimized at the scale of the neighborhood. For the neighborhood case, buildings can also be connected together through a heat network. We use a detailed three-dime ...

IOP Publishing Ltd2019

Small Extended Formulation for Knapsack Cover Inequalities from Monotone Circuits

Ola Nils Anders Svensson, Abbas Bazzi, Sangxia Huang

Initially developed for the min-knapsack problem, the knapsack cover inequalities are used in the current best relaxations for numerous combinatorial optimization problems of covering type. In spite of their widespread use, these inequalities yield linear ...

UNIV CHICAGO, DEPT COMPUTER SCIENCE2018

Integrating advanced discrete choice models in mixed integer linear optimization

Michel Bierlaire, Shadi Sharif Azadeh, Meritxell Pacheco Paneque, Bernard Gendron

The integration of choice models in mixed integer linear programming (MILP) is appealing to operators because it provides a better understanding of the preferences of customers while planning for their systems. However, the complexity of choice models lea ...

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From Uncertainty Data to Robust Policies for Temporal Logic Planning

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We consider the problem of synthesizing robust disturbance feedback policies for systems performing complex tasks. We formulate the tasks as linear temporal logic specifications and encode them into an optimization framework via mixed-integer constraints. ...

ACM2018

A Constant-Factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem

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Lagrangian relaxation for the demand-based benefit maximization problem

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The integration of discrete choice models in Mixed Integer Linear Programming (MILP) models provides a better understanding of customers' preferences to operators while planning for their systems. However, the formulations associated with the choice model ...

2018

Lagrangian relaxation for the demand-based benefit maximization problem

Michel Bierlaire, Shadi Sharif Azadeh, Virginie Janine Camille Lurkin, Meritxell Pacheco Paneque, Bernard Gendron

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Data-Driven Chance Constrained Programs over Wasserstein Balls

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We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed ...

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Relaxation continue

An adaptive primal-dual framework for nonsmooth convex minimization

Retrofitting, district heating and energy storage: neighborhood energy planning

Small Extended Formulation for Knapsack Cover Inequalities from Monotone Circuits

Integrating advanced discrete choice models in mixed integer linear optimization

From Uncertainty Data to Robust Policies for Temporal Logic Planning

A Constant-Factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem

Lagrangian relaxation for the demand-based benefit maximization problem

Lagrangian relaxation for the demand-based benefit maximization problem

Algorithms For Clustering Problems

Data-Driven Chance Constrained Programs over Wasserstein Balls

Graph Chatbot

Chattez avec Graph Search

Small Extended Formulation for Knapsack Cover Inequalities from Monotone Circuits

A Constant-Factor Approximation Algorithm for the Asymmetric Traveling Salesman Problem

Lagrangian relaxation for the demand-based benefit maximization problem

From Uncertainty Data to Robust Policies for Temporal Logic Planning

Lagrangian relaxation for the demand-based benefit maximization problem

Data-Driven Chance Constrained Programs over Wasserstein Balls

Algorithms For Clustering Problems

An adaptive primal-dual framework for nonsmooth convex minimization

Retrofitting, district heating and energy storage: neighborhood energy planning

Integrating advanced discrete choice models in mixed integer linear optimization