Results 81 to 90 of about 49,656 (213)
Finding a positive semidefinite interval for a parametric matrix
Linear Algebra and its Applications, 1986 For given symmetric \(n\times n\) matrices, positive semidefinite C and E of rank one or two, real numbers ṯ\(\leq 0\), \(\bar t\geq 0\) are obtained such that the parametric matrix \(C(t)=C+tE\) is positive semidefinite if and only if \(t\in [\underline t,\bar t]\).
Caron, R.J., Gould, N.I.M.
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Ethnic Conflicts, Civil War, and Economic Growth: Region‐Level Evidence From Former Yugoslavia
Journal of Regional Science, EarlyView.ABSTRACT This paper studies the long‐term effects of the Yugoslav civil war (1987–1995) on subnational economic growth across 78 regions in five former Yugoslav republics from 1950 to 2015. We construct counterfactual growth trajectories using a robust region‐level donor pool from 32 conflict‐free countries.
Aleksandar Kešeljević +2 more
wiley +1 more source
RisksThis paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable ...
Jiro Akahori +5 more
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Nihon Kikai Gakkai ronbunshu, 2015 We propose a Newton-gradient-hybrid optimization method for fluid topology optimization. The method accelerates convergence and reduces computation time. In addition, the fluid-solid boundaries are clearly distinguished.
Kazuo YONEKURA, Yoshihiro KANNO
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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
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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
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Singular Value and Matrix Norm Inequalities between Positive Semidefinite Matrices and Their Blocks
Journal of MathematicsIn this paper, we obtain some inequalities involving positive semidefinite 2×2 block matrices and their blocks.
Feng Zhang +3 more
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Structural Results on the HMLasso
AxiomsHMLasso (Lasso with High Missing Rate) is a useful technique for sparse regression when a high-dimensional design matrix contains a large number of missing data. To solve HMLasso, an appropriate positive semidefinite symmetric matrix must be obtained. In
Shin-ya Matsushita, Hiromu Sasaki
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Homogeneous Observer‐Based Affine Formation Tracking
International Journal of Robust and Nonlinear Control, Volume 36, Issue 5, Page 2692-2704, 25 March 2026.ABSTRACT This article addresses the control of mobile agents, termed followers, to track a time‐varying affine formation specified by a set of leaders. We present a distributed hierarchical method composed of a homogeneous high‐order sliding mode observer and a tracking controller. The observer estimates the followers' target trajectories from neighbor
Rodrigo Aldana‐López +3 more
wiley +1 more source
Correlation Clustering with Low-Rank Matrices
, 2017 Correlation clustering is a technique for aggregating data based on qualitative information about which pairs of objects are labeled 'similar' or 'dissimilar.' Because the optimization problem is NP-hard, much of the previous literature focuses on ...
Arthur D. +7 more
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