Results 111 to 120 of about 43,906 (221)

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

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

Reinforcement Learning for Jump‐Diffusions, With Financial Applications

Mathematical Finance, EarlyView.
ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
wiley   +1 more source

Foreign Exchange Regimes in (Normal Times and) Times of War: Insights From Ukraine

Scottish Journal of Political Economy, EarlyView.
ABSTRACT On February 24, 2022, as Russia invaded, the National Bank of Ukraine switched from a flexible to a fixed‐exchange rate regime. Was this optimal? We develop a tractable but carefully calibrated open‐economy model of Ukraine with nominal rigidities and frictions in international financial markets.
Oliver de Groot, Yevhenii Skok
wiley   +1 more source

On some features of quadratic unconstrained binary optimization with random coefficients

Bollettino dell'Unione Matematica Italiana
Abstract Quadratic Unconstrained Binary Optimization (QUBO or UBQP) is concerned with maximizing/minimizing the quadratic form $$H(J, \eta ) = W \sum _{i,j} J_{i,j} \eta _{i} \eta _{j}$$ H ...
Isopi, Marco, Scoppola, Benedetto, Troiani, Alessio   +2 more
openaire   +5 more sources

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

Why raw yield data are better than relative yield in informing agronomic and economic decisions

Agricultural &Environmental Letters, Volume 11, Issue 1, June 2026.
Abstract While relative yield is widely used for its comparability, normalization can cause significant information loss. This study reframes yield metric selection as a model evaluation problem to determine the most accurate representation of crop response. We evaluated seven yield metrics with three agronomic models, comparing estimates of a critical
Falin Sun, Wenying Li, Joshua M. Duke, Joshua M. McGrath, Vaughn Reed   +4 more
wiley   +1 more source

Topology Optimization in Civil Engineering − On the Consideration of Concrete Failure Characteristic and Self‐Weight

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT The mechanical efficiency of civil engineering structures is a key factor in the sustainable transformation of the building sector. This study presents topology optimization in civil engineering design. The static load profile of civil engineering structures is typically dominated by their self‐weight, which introduces, as a special ...
Daniela Masarczyk, Minh Tuan Tran, Detlef Kuhl   +2 more
wiley   +1 more source

Recursive Feasibility of Nonlinear Stochastic Model Predictive Control With Gaussian Process Dynamics

International Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 4957-4970, June 2026.
ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf, Andreas Völz, Knut Graichen   +2 more
wiley   +1 more source

A Polynomial-Time Algorithm for Unconstrained Binary Quadratic Optimization

, 2020
In this paper, an exact algorithm in polynomial time is developed to solve unrestricted binary quadratic programs. The computational complexity is $O\left( n^{\frac{15}{2}}\right) $, although very conservative, it is sufficient to prove that this minimization problem is in the complexity class $P$.
openaire   +2 more sources