Results 71 to 80 of about 18,556 (186)

Left-deep join order selection with higher-order unconstrained binary optimization on quantum computers

Frontiers in Computer Science
Join order optimization is among the most crucial query optimization problems, and its central position is also evident in the new research field where quantum computing is applied to database optimization and data management.
Valter Uotila
doaj   +1 more source

Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions

, 2020
This study investigates the optimization aspects of imposing hard inequality constraints on the outputs of CNNs. In the context of deep networks, constraints are commonly handled with penalties for their simplicity, and despite their well-known ...
Ayed, Ismail Ben, Desrosiers, Christian, Dolz, Jose, Granger, Eric, Kervadec, Hoel, Yuan, Jing   +5 more
core  

The Great Temporal Divide: How Top Management Team Temporal Faultlines and Dominant Subgroups Shape Firm Innovativeness in Iran

Journal of Management Studies, EarlyView.
Abstract While executives vary in attention to the past, present, and future, prior work has largely examined these temporal orientations in isolation or at the individual level, which limits insight into how they jointly configure within top management teams (TMTs) and translate into firm behaviours.
Shi Tang, Sherry M. B. Thatcher, Andreas W. Richter, Stephen X. Zhang, Asghar Afshar Jahanshahi   +4 more
wiley   +1 more source

Rydberg‐Atom Graphs for Quadratic Unconstrained Binary Optimization Problems

Advanced Quantum Technologies
Abstract There is a growing interest in harnessing the potential of the Rydberg‐atom system to address complex combinatorial optimization challenges. Here an experimental demonstration of how the quadratic unconstrained binary optimization (QUBO) problem can be effectively addressed using Rydberg‐atom graphs is presented.
Andrew Byun, Junwoo Jung, Kangheun Kim, Minhyuk Kim, Seokho Jeong, Heejeong Jeong, Jaewook Ahn   +6 more
openaire   +2 more sources

Efficient rank minimization to tighten semidefinite programming for unconstrained binary quadratic optimization [PDF]

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2017
We propose a method for low-rank semidefinite programming in application to the semidefinite relaxation of unconstrained binary quadratic problems. The method improves an existing solution of the semidefinite programming relaxation to achieve a lower rank solution.
Roman Pogodin, Mikhail Krechetov, Yury Maximov   +2 more
openaire   +2 more sources

Welfare implications of fair and accountable insurance pricing

Journal of Risk and Insurance, EarlyView.
Abstract This paper introduces an empirical framework to evaluate the welfare implications of fair and accountable insurance pricing by modeling the complete pricing process, including demand and price optimization. Moving beyond traditional cost modeling, we analyze both discrimination‐related fairness criteria and broader regulatory constraints, such
Fei Huang, Hajime Shimao
wiley   +1 more source

Markov Determinantal Point Process for Dynamic Random Sets

Journal of Time Series Analysis, EarlyView.
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley   +1 more source

A novel dual‐decomposition method for non‐convex two‐stage stochastic mixed‐integer quadratically constrained quadratic problems

International Transactions in Operational Research, Volume 33, Issue 5, Page 3128-3157, September 2026.
Abstract We propose the novel p‐branch‐and‐bound method for solving two‐stage stochastic programming problems whose deterministic equivalents are represented by non‐convex mixed‐integer quadratically constrained quadratic programming (MIQCQP) models. The precision of the solution generated by the p‐branch‐and‐bound method can be arbitrarily adjusted by
Nikita Belyak, Fabricio Oliveira
wiley   +1 more source

Quadratic unconstrained binary optimization for environmental control system equipment design and assessment

Advanced Modeling and Simulation in Engineering Sciences
This paper explores possible implementations of a quantum annealing-based algorithm, in the Quadratic unconstrained binary optimization method (QUBO) form, to solve the thermo-fluid dynamics problem associated with the design of critical components of ...
Giulio Malinverno, Javier Blasco Alberto
doaj   +1 more source

RinQ: Towards predicting central sites in proteins on current quantum computers

Materials Today Quantum
We introduce RinQ, a hybrid quantum–classical framework for identifying functionally critical residues in proteins by formulating centrality detection as a Quadratic Unconstrained Binary Optimization (QUBO) problem.
Shah Ishmam Mohtashim
doaj   +1 more source