In recent years, robust Markov decision processes (MDPs) have emerged as a prominent
modeling framework for dynamic decision problems affected by uncertainty. In contrast to …

The graph partition problem (GPP) aims at clustering the vertex set of a graph into a fixed
number of disjoint subsets of given sizes such that the sum of weights of edges joining …

The Fantope-constrained sparse principal subspace estimation problem is initially proposed
by Vu et al.(Vu et al., 2013). This paper investigates a semismooth Newton based proximal …

We consider a two-person network inspection game, in which a defender positions a limited
number of detectors to detect multiple attacks caused by an attacker. We assume that …

In this paper, we will provide a detailed explanation of the limitations behind differentiable
architecture search (DARTS). Algorithms based on the DARTS paradigm tend to converge …

The neural architecture search (NAS) is characterized by a wide search space and a time
consuming objective function. Many papers have dealt with the reduction of the cost of the …

Many real-life decision problems are discrete in nature. To solve such problems as
mathematical optimization problems, integrality constraints are commonly incorporated in …

Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings
from augmented graphs, has become a prevailing method for extracting embeddings from …

Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings
from augmented graphs, has become a prevailing method for learning embeddings from …