This study proposes a mixed-integer programming approach to minimize such switches while ensuring water quality and satisfying drinking water tank capacity constraints. Considering daily operations, ...

In this paper, we tackle the problem of localizing graphical symbols on complex technical document images by using an original approach to solve the subgraph isomorphism problem. In the proposed ...

Handling noisy data is a persistent issue in machine learning, but it takes on a unique complexity in the context of graph structures. In domains where graph data is prevalent—such as social networks, ...

IJCNN 2025 Competition: Learning with Noisy Graph Labels Handling noisy data is a persistent issue in machine learning, but it takes on a unique complexity in the context of graph structures.

This paper gives integer linear programming (ILP) models for scheduling the League Phase of one of the most popular professional club competitions in the world, UEFA Champion’s League. There are 36 ...

Graph algorithms are integral for solving complex programming challenges. They provide methods for handling data relationships through graphs, which are essential in fields like network analysis, ...

Navigating rigid body objects through crowded environments can be challenging, especially when narrow passages are presented. Existing sampling-based planners and optimization-based methods like mixed ...

Integer Linear Programming (ILP) is the foundation of combinatorial optimization, which is extensively applied across numerous industries to resolve challenging decision-making issues. Under a set of ...

Integer [Linear] Programming Algorithms for integer programming often take a first stab at a solution with linear programming, which is outwardly similar but allows the variables to vary continuously.

The idea of training-free Graph Neural Networks (TFGNNs) has been presented as a solution to these problems. During transductive node classification, TFGNNs use the concept of “labels as features” ...