cog-imperial/moo_trees

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Python
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BSD 3-Clause "New...
Created about 3 years ago
Updated about 3 years ago

cog-imperial/moo_trees

cog-imperial

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OMLT

Represent trained machine learning models as Pyomo optimization formulations

romodel

Modeling robust optimization problems in Pyomo

entmoot

Multiobjective black-box optimization using gradient-boosted trees

galini

An extensible MINLP solver

GPdoemd

Design of experiments for model discrimination using Gaussian process surrogate models

suspect

Special Structure Detection for Pyomo

SnAKe

Repository for paper: "SnAKe: Bayesian Optimization with Pathwise Exploration".

pooling-network

tree_kernel_gp

min_matches_heuristics

Source code of the methods proposed in "Dimitrios Letsios, Georgia Kouyialis, Ruth Misener. Heuristics with Performance Guarantees for the Minimum Number of Matches Problem in Heat Recovery Network Design. Computers and Chemical Engineering 113:57-85, 2018".".

pypopt

Bindings to Ipopt C++ library for Python. 🐍

rogp

Modeling Gaussian Processes in Pyomo

PartitionedFormulations_NN

Implementation of partition-based formulations for mixed-integer optimization of trained ReLU neural networks

two_stage_scheduling

Exact lexicographic scheduling methods and approximate recovery strategies for two-stage makespan scheduling

GNN_MIP_CAMD

Repository for paper: "Optimizing over trained GNNs via symmetry breaking".

OMLT_CAMD

Repository for paper: "Augmenting optimization-based molecular design with graph neural networks".

galini-dashboard

Dashboard for the GALINI Solver

DCBO

Repository for paper: "Dependence in constrained Bayesian optimization: When do we need it and how does it help?".

Partial-Lasserre-relaxation-sparse-maxcut

drill-scheduling

Pyomo implementation of a drill scheduling case study