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 ...

Computer-generated holography (CGH) provides an approach to digitally modulate a given wavefront. This technology, partly inherited from optical holography and partly advanced by the progress of ...

In this paper we test different conjugate gradient (CG) methods for solving largescale unconstrained optimization problems. The methods are divided in two groups: the first group includes five basic ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

In an era where artificial intelligence swiftly evolves and redefines the boundaries of possibility, Google DeepMind has once again taken ...

In modern computing, solving complex optimization problems has always been a significant challenge. Recently, a research team from Canada developed a new type of photonic Ising machine capable of ...

Problem solving is, itself, a problem. With so many proven methods at our disposal, choosing the right one can feel like a long and difficult journey. Today, I’d like to take a look at three methods ...