Search Results for author:
Found 12 papers, 4 papers with code
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.
Robust Optimal Control With Binary Adjustable Uncertainties
Robust Optimal Control (ROC) with adjustable uncertainties has proven to be effective in addressing critical challenges within modern energy networks, especially the reserve and provision problem.
Unlocking Energy Flexibility From Thermal Inertia of Buildings: A Robust Optimization Approach
In a first step, a robust optimization model is formulated for assessing the energy flexibility of buildings in the presence of uncertain predictions of external conditions, such as ambient temperature, solar irradiation, etc.
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems.
Robust Losses for Decision-Focused Learning
To understand the impact of this deficiency, we evaluate the effect of aleatoric and epistemic uncertainty on the accuracy of empirical regret as a surrogate.
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks
We study the case of few-bit discrete-valued neural networks, both Binarized Neural Networks (BNNs), whose values are restricted to +-1, and Integer Neural Networks (INNs), whose values lie in a range {-P, ..., P}.
Learning to branch with Tree MDPs
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule.
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
Optimal training of integer-valued neural networks with mixed integer programming
The second method addresses the amount of training data which MIP can feasibly handle: we provide a batch training method that dramatically increases the amount of data that MIP solvers can use to train.
A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP
In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy.
Towards a Framework for Certification of Reliable Autonomous Systems
A computational system is called autonomous if it is able to make its own decisions, or take its own actions, without human supervision or control.
Order Acceptance and Scheduling with Sequence-dependent Setup Times: a New Memetic Algorithm and Benchmark of the State of the Art
Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS.
