110 citations · 185 across the 21 of their papers we have counts for
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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…