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Concept

Sequential quadratic programming

Related publications (28)

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Michel Bierlaire

In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...

2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

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

Constrained Form-Finding of Tension-Compression Structures using Automatic Differentiation

Stefana Parascho

This paper proposes a computational approach to form-find pin-jointed bar structures subjected to combinations of tension and compression forces. The generated equilibrium states can meet structural and geometrical constraints via gradient-based optimizati ...

ELSEVIER SCI LTD2023

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Colin Neil Jones, Yuning Jiang, Yingzhao Lian

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

IEEE2023

High-dimensional optimization under nonconvex excluded volume constraints

Antonio Sclocchi

We consider high-dimensional random optimization problems where the dynamical variables are subjected to nonconvex excluded volume constraints. We focus on the case in which the cost function is a simple quadratic cost and the excluded volume constraints a ...

AMER PHYSICAL SOC2022

A combined Control by Interconnection—Model Predictive Control design for constrained Port-Hamiltonian systems

Anna Ngoc Minh Trang Vu

This paper proposes a Control by Interconnection design, for a class of constrained Port-Hamiltonian systems, which is based on an associated Model Predictive Control optimization problem. This associated optimization problem allows to consider both state ...

2022

Design of structures that adapt to loads through large shape changes

Ian Smith, Gennaro Senatore, Arka Prabhata Reksowardojo

Adaptive structures can modify their geometry and internal forces through sensing and mechanical actuation in order to maintain optimal performance under changing actions. Previous work has shown that well-conceived adaptive design strategies achieve subst ...

2020

TV-based reconstruction of periodic functions

Julien René Fageot, Matthieu Martin Jean-André Simeoni

We introduce a general framework for the reconstruction of periodic multivariate functions from finitely many and possibly noisy linear measurements. The reconstruction task is formulated as a penalized convex optimization problem, taking the form of a sum ...

IOP PUBLISHING LTD2020

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 ...

2020

On Certifying Non-Uniform Bounds against Adversarial Attacks

Volkan Cevher, Chen Liu

This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In contrast to the existing approaches that seek regions bounded uniformly along a ...

2019

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Ilija Bogunovic

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming

Volkan Cevher, Alp Yurtsever

We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines the notions of smoothing and homotopy under the CGM framework, and provably achieves the optimal O(1/sqrt(k)) convergenc ...

ICML2018

A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization

Volkan Cevher, Quoc Tran Dinh

We propose a new and low per-iteration complexity first-order primal-dual optimization framework for a convex optimization template with broad applications. Our analysis relies on a novel combination of three classic ideas applied to the primal-dual gap fu ...

SIAM Publications2018

Integration of passenger satisfaction in railway timetable rescheduling for major disruptions

Stefan Yves Gabriel Binder

Unexpected disruptions occur for many reasons in railway networks and cause delays, cancellations, and, eventually, passenger inconvenience. This thesis focuses on the railway timetable rescheduling problem from a macroscopic point of view in case of lar ...

EPFL2018

Methodologies for simultaneous optimization of heat, mass, and power in industrial processes

Maziar Kermani

Efficient consumption of energy and material resources, including water, is the primary focus for process industries to reduce their environmental impact. The Conference of Parties in Paris (COP21) highlighted the prominent role of industrial energy effici ...

EPFL2018

An Alternating Trust Region Algorithm for Distributed Linearly Constrained Nonlinear Programs, Application to the Optimal Power Flow Problem

Colin Neil Jones, Jean-Hubert Hours

A novel trust region method for solving linearly constrained nonlinear programs is presented. The proposed technique is amenable to a distributed implementation, as its salient ingredient is an alternating projected gradient sweep in place of the Cauchy po ...

2017

Technical Report: Voltage Source Converter MPC with Optimized Pulse Patterns and minimization of Integrated Squared Tracking Error

Ivan Pejcic

Model predictive control schemes for power electronic applications are characterized by a great variety of problem formulations. In this paper, we consider a three phase voltage source converter with an arbitrary number of voltage levels and derive a model ...

2017

A multi-agent based analytical approach for service restoration in distribution networks

Sidi-Rachid Cherkaoui, Hossein Sekhavatmanesh

With the advent of smart grids and the higher level of requirement for the quality of supply, restoration of distribution networks is a timely topic that deserves to be revisited. In this paper, the concept of multi-agent automation in smart grids is appli ...

IEEE2017

Decomposition Strategies for Nonconvex Problems, a Parametric Approach

Jean-Hubert Hours

This thesis deals with the development of numerical methods for solving nonconvex optimisation problems by means of decomposition and continuation techniques. We first introduce a novel decomposition algorithm based on alternating gradient projections and ...

EPFL2016

Optimal design methods applied to solar-assisted hydrothermal gasification plants

Alberto Mian

The gasification of highly watered waste residues may help to reduce fossil fuels consumption, as well as emissions, all the while improving efficiency. The integration of supercritical gasification with solar energy may improve the performances of both ne ...

EPFL2016