Multi-language evaluation of exact solvers in graphical model discrete optimization
By representing the constraints and objective function in factorized form, graphical models
can concisely define various NP-hard optimization problems. They are therefore extensively …
can concisely define various NP-hard optimization problems. They are therefore extensively …
Guaranteed discrete energy optimization on large protein design problems
In Computational Protein Design (CPD), assuming a rigid backbone and amino-acid rotamer
library, the problem of finding a sequence with an optimal conformation is NP-hard. In this …
library, the problem of finding a sequence with an optimal conformation is NP-hard. In this …
Valued constraint satisfaction problems
As an extension of constraint networks, valued constraint networks (or valued CSPs) define
a unifying framework for modelling optimisation problems over finite domains in which the …
a unifying framework for modelling optimisation problems over finite domains in which the …
[HTML][HTML] Tractability-preserving transformations of global cost functions
Graphical model processing is a central problem in artificial intelligence. The optimization of
the combined cost of a network of local cost functions federates a variety of famous problems …
the combined cost of a network of local cost functions federates a variety of famous problems …
Multiple-choice knapsack constraint in graphical models
P Montalbano, S de Givry, G Katsirelos - International Conference on …, 2022 - Springer
Graphical models, such as cost function networks (CFNs), can compactly express large
decomposable functions, which leads to efficient inference algorithms. Most methods for …
decomposable functions, which leads to efficient inference algorithms. Most methods for …
Triangle-based consistencies for cost function networks
Abstract Cost Function Networks (aka Weighted CSP) allow to model a variety of problems,
such as optimization of deterministic and stochastic graphical models including Markov …
such as optimization of deterministic and stochastic graphical models including Markov …
Solving the Crop Allocation Problem using Hard and Soft Constraints∗
M Akplogan, S De Givry, JP Metivier… - RAIRO-Operations …, 2013 - cambridge.org
Application tools for the crop allocation problem (CAP) are required for agricultural advisors
to design more efficient farming systems. Despite the extensive treatment of this issue by …
to design more efficient farming systems. Despite the extensive treatment of this issue by …
Exact and approximate inference in graphical models: variable elimination and beyond
Probabilistic graphical models offer a powerful framework to account for the dependence
structure between variables, which is represented as a graph. However, the dependence …
structure between variables, which is represented as a graph. However, the dependence …
Exact or approximate inference in graphical models: why the choice is dictated by the treewidth, and how variable elimination can be exploited
Probabilistic graphical models offer a powerful framework to account for the dependence
structure between variables, which is represented as a graph. However, the dependence …
structure between variables, which is represented as a graph. However, the dependence …
Including soft global constraints in DCOPs
C Bessiere, P Gutierrez, P Meseguer - International Conference on …, 2012 - Springer
In the centralized context, global constraints have been essential for the advancement of
constraint reasoning. In this paper we propose to include soft global constraints in …
constraint reasoning. In this paper we propose to include soft global constraints in …