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Scalable constrained optimization

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Scalable Convex Optimization Methods for Semidefinite Programming

Alp Yurtsever

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, contemporary applications in science and engineering impose heavy computational and storage burdens on the optimization algorithms. As a result, there ...

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110th Anniversary: A Feature-Based Analysis of Static Real-Time Optimization Schemes

Dominique Bonvin

This paper addresses the steady-state optimization of continuous processes in the presence of uncertainty in the form of unknown or time-varying model parameters, structural plant-model mismatch, and disturbances. To address these issues, we assume that ce ...

AMER CHEMICAL SOC2019

Designing Coalition-Proof Reverse Auctions Over Continuous Goods

Maryam Kamgarpour, Orcun Karaca

This paper investigates reverse auctions that involve continuous values of different types of goods, general nonconvex constraints, and second stage costs. We seek to design the payment rules and conditions under which coalitions of participants cannot inf ...

2019

This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...

IEEE2018

Multi-objective optimization tools are becoming increasingly popu-lar in mechanical engineering and allow decision-makers to better understand the inevitable trade-offs. Mechanical design problems can however combine properties that make the use of optimiz ...

2018

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

2018

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IEEE2018

We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particular, our algorithm can be specialized to invert positive definite matrices i ...

2018

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Online Adaptive Methods, Universality and Acceleration

Volkan Cevher, Alp Yurtsever

We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard converg ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018