13 citations · 22 across the 10 of their papers we have counts for
10 papers
ADMM-based decomposed DNN+RLT Relaxations for Completely Positive Models in Electricity Market Clearing
Shudian Zhao, Mohammad Reza Karimi Gharigh, Jan Kronqvist +1
The day-ahead electricity market clearing with nonconvex order types can be formulated as a mixed-integer linear program (MILP), but its LP relaxation may provide weak bounds, and…
Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
Miguel Angel Lopez-Ruiz, Daiwei Zhu, Jonas Hatzenbuhler +8
We present a quantum optimization framework for the Shipment Selection Problem (SSP) in electric freight logistics, developed jointly by IonQ and Einride. Idle gaps arising from st…
A Framework for Eliminating Paradoxical Orders in European Day-Ahead Electricity Markets through Mixed-Integer Linear Programming Strong Duality
Zhen Wang, Mohammad Reza Hesamzadeh, Shudian Zhao +1
The presence of integer variables in the European day-ahead electricity market renders the social welfare maximization problem non-convex and non-differentiable, making classical m…
A constrained optimization approach to improve robustness of neural networks
Shudian Zhao, Jan Kronqvist
In this paper, we present a novel nonlinear programming-based approach to fine-tune pre-trained neural networks to improve robustness against adversarial attacks while maintaining…
Ground truth clustering is not the optimum clustering
Lucia Absalom Bautista, Timotej Hrga, Janez Povh +1
The clustering of data is one of the most important and challenging topics in data science. The minimum sum-of-squares clustering (MSSC) problem asks to cluster the data points int…
Alternating mixed-integer programming and neural network training for approximating stochastic two-stage problems
Jan Kronqvist, Boda Li, Jan Rolfes +1
The presented work addresses two-stage stochastic programs (2SPs), a broadly applicable model to capture optimization problems subject to uncertain parameters with adjustable decis…
Model-based feature selection for neural networks: A mixed-integer programming approach
Shudian Zhao, Calvin Tsay, Jan Kronqvist
In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach. While the me…
Partitioning through projections: strong SDP bounds for large graph partition problems
Frank de Meijer, Renata Sotirov, Angelika Wiegele +1
The graph partition problem (GPP) aims at clustering the vertex set of a graph into a fixed number of disjoint subsets of given sizes such that the sum of weights of edges joining…
Tight SDP relaxations for cardinality-constrained problems
Angelika Wiegele, Shudian Zhao
We model the cardinality-constrained portfolio problem using semidefinite matrices and investigate a relaxation using semidefinite programming. Experimental results show that this…
SDP-based bounds for graph partition via extended ADMM
Angelika Wiegele, Shudian Zhao
We study two NP-complete graph partition problems, -equipartition problems and graph partition problems with knapsack constraints (GPKC). We introduce tight SDP relaxations with…