110 citations · 185 across the 21 of their papers we have counts for
21 papers
Input convex neural networks as surrogates in mathematical optimisation
Yu Liu, Jan Kronqvist, Fabricio Oliveira
Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research. The prevailing approach uses feedforward neural netw…
Separation, Constraint Qualifications, and Cycling in Outer Approximation
Erik Tamm, Jan Kronqvist
The outer approximation algorithm is a widely used method for solving convex mixed-integer nonlinear programs. While the algorithm is well established in theory and practice, certa…
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…
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…
Warm-starting outer approximation for parameterized convex MINLP
Erik Tamm, Gabriele Eichfelder, Jan Kronqvist
We address the challenge of efficiently solving parameterized sequences of convex Mixed-Integer Nonlinear Programming (MINLP) problems through warm-starting techniques. We focus on…
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…
A GPU-Accelerated Bi-linear ADMM Algorithm for Distributed Sparse Machine Learning
Alireza Olama, Andreas Lundell, Jan Kronqvist +2
This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problem…
A cutting plane algorithm for globally solving low dimensional k-means clustering problems
Martin Ryner, Jan Kronqvist, Johan Karlsson
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods. There is a variety of approxima…
Solution Polishing via Path Relinking for Continuous Black-Box Optimization
Dimitri Papageorgiou, Jan Kronqvist, Asha Ramanujam +3
When faced with a limited budget of function evaluations, state-of-the-art black-box optimization (BBO) solvers struggle to obtain globally, or sometimes even locally, optimal solu…
LineWalker: Line Search for Black Box Derivative-Free Optimization and Surrogate Model Construction
Dimitri J. Papageorgiou, Jan Kronqvist, Krishnan Kumaran
This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a multi-dimensional function along a one-dimensi…
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
Martin Ryner, Jan Kronqvist, Johan Karlsson
This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean no…
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…
A Column Generation Approach for Radiation Therapy Patient Scheduling with Planned Machine Unavailability and Uncertain Future Arrivals
Sara Frimodig, Per Enqvist, Jan Kronqvist
The number of cancer cases per year is rapidly increasing worldwide. In radiation therapy (RT), radiation from linear accelerators is used to kill malignant tumor cells. Scheduling…
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…
A mixed-integer approximation of robust optimization problems with mixed-integer adjustments
Jan Kronqvist, Boda Li, Jan Rolfes
In the present article we propose a mixed-integer approximation of adjustable-robust optimization (ARO) problems, that have both, continuous and discrete variables on the lowest le…
Sparse Convex Optimization Toolkit: A Mixed-Integer Framework
Alireza Olama, Eduardo Camponogara, Jan Kronqvist
This paper proposes an open-source distributed solver for solving Sparse Convex Optimization (SCO) problems over computational networks. Motivated by past algorithmic advances in m…
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
Jan Kronqvist, Ruth Misener, Calvin Tsay
We develop a class of mixed-integer formulations for disjunctive constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength. The main idea i…
Maximizing information from chemical engineering data sets: Applications to machine learning
Alexander Thebelt, Johannes Wiebe, Jan Kronqvist +2
It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for m…
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks
Calvin Tsay, Jan Kronqvist, Alexander Thebelt +1
This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. The approach balances model size and tightness by partitioning node inputs into a numb…
Between steps: Intermediate relaxations between big-M and convex hull formulations
Jan Kronqvist, Ruth Misener, Calvin Tsay
This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing advantages from both. The proposed "P-split" formulations split…
ENTMOOT: A Framework for Optimization over Ensemble Tree Models
Alexander Thebelt, Jan Kronqvist, Miten Mistry +3
Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important pred…