In computational complexity theory, P and NP are two classes of problems. P is the class of decision problems that a deterministic Turing machine can solve in polynomial time. In useful terms, any ...

Graph theory has long provided a robust mathematical framework for investigating networks, relations and connectivity in both abstract and applied settings. Recent advances have markedly refined our ...

Constraint satisfaction problems (CSPs) provide a versatile framework for modelling complex decision-making tasks where a collection of variables must be allocated values that satisfy specific ...

Memcomputing is a novel non-Turing paradigm of computation that uses interacting memory cells (memprocessors for short) to store and process information on the same physical platform. It was recently ...

Quantum computers get a lot of people excited because they solve problems in a manner that’s fundamentally different from existing hardware. A certain class of mathematical problems, called ...

They have a mathematical, analog “solver” that can potentially find the best solution to NP-hard problems. NP-hardness is a theory of computational complexity, with problems that are famous for their ...

The historical pursuit of creating intelligent machines has culminated in the modern era of artificial intelligence. However, the efficacy of AI applications is contingent upon a nuanced understanding ...

NP-complete problems, including optimal routing, scheduling and network design, are foundational to essential tasks across various industries. However, they actually pose challenges for conventional ...