Decision diagrams for discrete optimization: A survey of recent advances
In the last decade, decision diagrams (DDs) have been the basis for a large array of novel
approaches for modeling and solving optimization problems. Many techniques now use DDs …
approaches for modeling and solving optimization problems. Many techniques now use DDs …
Improving variable orderings of approximate decision diagrams using reinforcement learning
Prescriptive analytics provides organizations with scalable solutions for large-scale,
automated decision making. At the core of prescriptive analytics methodology is …
automated decision making. At the core of prescriptive analytics methodology is …
The expressive power of ad-hoc constraints for modelling CSPs
Ad-hoc constraints (also called generic constraints) are important for modelling Constraint
Satisfaction Problems (CSPs). Many representations have been proposed to define ad-hoc …
Satisfaction Problems (CSPs). Many representations have been proposed to define ad-hoc …
An introduction to decision diagrams for optimization
WJ van Hoeve - … Research: Smarter Decisions for a Better …, 2024 - pubsonline.informs.org
This tutorial provides an introduction to the use of decision diagrams for solving discrete
optimization problems. A decision diagram is a graphical representation of the solution …
optimization problems. A decision diagram is a graphical representation of the solution …
Optimal decision diagrams for classification
Decision diagrams for classification have some notable advantages over decision trees, as
their internal connections can be determined at training time and their width is not bound to …
their internal connections can be determined at training time and their width is not bound to …
Peel-and-bound: Generating stronger relaxed bounds with multivalued decision diagrams
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete
optimization. However, the field of decision diagrams is relatively new, and is still …
optimization. However, the field of decision diagrams is relatively new, and is still …
Generalized arc consistency algorithms for table constraints: A summary of algorithmic ideas
Constraint Programming is a powerful paradigm to model and solve combinatorial problems.
While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps …
While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps …
Computing AES related-key differential characteristics with constraint programming
Cryptanalysis aims at testing the properties of encryption processes, and this usually implies
solving hard optimization problems. In this paper, we focus on related-key differential attacks …
solving hard optimization problems. In this paper, we focus on related-key differential attacks …
Improved Peel-and-Bound: Methods for generating dual bounds with multivalued decision diagrams
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete
optimization. However, the field of decision diagrams is relatively new, and is still …
optimization. However, the field of decision diagrams is relatively new, and is still …
Encoding multi-valued decision diagram constraints as binary constraint trees
Abstract Ordered Multi-valued Decision Diagram (MDD) is a compact representation used to
model various constraints, such as regular constraints and table constraints. It can be …
model various constraints, such as regular constraints and table constraints. It can be …