We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

We might be witnessing the start of a new computing era where AI, cloud and quantum begin to converge in ways that redefine ...

Problem Modeling: the first step involves modeling the combinatorial optimization problem by clearly defining the objective function, constraints, and candidate elements. This step forms the ...

Traffic modeling has been of interest to mathematicians since the 1950s. Research in the area has only grown as road traffic control presents an ever-increasing problem. In a new paper, authors ...