Results 81 to 90 of about 41,020 (249)
A Similarity Matrix for Preserving Haplotype Diversity Amongst Parents in Genomic Selection
Journal of Animal Breeding and Genetics, EarlyView.ABSTRACT In genomic selection, balancing genetic gain with the preservation of genetic diversity is a critical challenge, requiring innovative approaches to parent selection. Traditional methods risk losing valuable genetic diversity by not fully accounting for the complex patterns of haplotype distribution.
Abdulraheem A. Musa, Norbert Reinsch
wiley +1 more source
On Positive Semidefinite Matrices with Known Null Space [PDF]
SIAM Journal on Matrix Analysis and Applications, 2002 We show how the zero structure of a basis of the null space of a positive semidefinite matrix can be exploited to determine a positive definite submatrix of maximal rank. We discuss consequences of this result for the solution of (constrained) linear systems and eigenvalue problems.
Peter Arbenz, Zlatko Drmač
openaire +4 more sources
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
, 2012 We consider a short rate model, driven by a stochastic process on the cone of positive semidefinite matrices.
ALESSANDRO GNOATTO +4 more
core +1 more source
Rough PDEs for Local Stochastic Volatility Models
Mathematical Finance, EarlyView.ABSTRACT In this work, we introduce a novel pricing methodology in general, possibly non‐Markovian local stochastic volatility (LSV) models. We observe that by conditioning the LSV dynamics on the Brownian motion that drives the volatility, one obtains a time‐inhomogeneous Markov process. Using tools from rough path theory, we describe how to precisely
Peter Bank +3 more
wiley +1 more source
A Pure Dual Approach for Hedging Bermudan Options
Mathematical Finance, EarlyView.ABSTRACT This paper develops a new dual approach to compute the hedging portfolio of a Bermudan option and its initial value. It gives a “purely dual” algorithm following the spirit of Rogers in the sense that it only relies on the dual pricing formula.
Aurélien Alfonsi +2 more
wiley +1 more source
, 2020 Semidefinite and sum-of-squares (SOS) optimization are fundamental computational tools in many areas, including linear and nonlinear systems theory. However, the scale of problems that can be addressed reliably and efficiently is still limited.
Papachristodoulou, Antonis +2 more
core
, 2016 Completely positive (CP) tensors, which correspond to a generalization of CP matrices, allow to reformulate or approximate a general polynomial optimization problem (POP) with a conic optimization problem over the cone of CP tensors.
Kuang, Xiaolong, Zuluaga, Luis F.
core +1 more source
Robust Λ$\Lambda$‐Quantiles and Extremal Distributions
Mathematical Finance, EarlyView.ABSTRACT In this paper, we investigate the robust models for Λ$\Lambda$‐quantiles with partial information regarding the loss distribution, where Λ$\Lambda$‐quantiles extend the classical quantiles by replacing the fixed probability level with a probability/loss function Λ$\Lambda$.
Xia Han, Peng Liu
wiley +1 more source
Positive Semidefinite Metric Learning Using Boosting-like Algorithms [PDF]
, 2012 The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data.
Hengel, Anton van den +3 more
core +3 more sources