Achieving an Arbitrary Spatial Stiffness with Springs Connected in Parallel

1998 ◽  
Vol 120 (4) ◽  
pp. 520-526 ◽  
Author(s):  
S. Huang ◽  
J. M. Schimmels
Keyword(s):  
Stiffness Matrix ◽  
Full Rank ◽  
Positive Definite Matrix ◽  
Positive Semidefinite ◽  
Positive Definite ◽  
The Other ◽  
Positive Semidefinite Matrix ◽  
Stiffness Matrices ◽  
Rotational Components ◽  
Semidefinite Matrix

In this paper, the synthesis of an arbitrary spatial stiffness matrix is addressed. We have previously shown that an arbitrary stiffness matrix cannot be achieved with conventional translational springs and rotational springs (simple springs) connected in parallel regardless of the number of springs used or the geometry of their connection. To achieve an arbitrary spatial stiffness matrix with springs connected in parallel, elastic devices that couple translational and rotational components are required. Devices having these characteristics are defined here as screw springs. The designs of two such devices are illustrated. We show that there exist some stiffness matrices that require 3 screw springs for their realization and that no more than 3 screw springs are required for the realization of full-rank spatial stiffness matrices. In addition, we present two procedures for the synthesis of an arbitrary spatial stiffness matrix. With one procedure, any rank-m positive semidefinite matrix is realized with m springs of which all may be screw springs. With the other procedure, any positive definite matrix is realized with 6 springs of which no more than 3 are screw springs.

scholarly journals A trace bound for integer-diagonal positive semidefinite matrices

2020 ◽  
Vol 8 (1) ◽  
pp. 14-16
Author(s):  
Lon Mitchell
Keyword(s):  
Positive Semidefinite ◽  
Positive Semidefinite Matrix ◽  
Positive Semidefinite Matrices ◽  
Semidefinite Matrix

AbstractWe prove that an n-by-n complex positive semidefinite matrix of rank r whose graph is connected, whose diagonal entries are integers, and whose non-zero off-diagonal entries have modulus at least one, has trace at least n + r − 1.

scholarly journals Refined Upper Solution Bound of the Continuous Coupled Algebraic Riccati Equation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Wang
Keyword(s):  
Riccati Equation ◽  
Positive Semidefinite ◽  
Algebraic Riccati Equation ◽  
Positive Semidefinite Matrix ◽  
Matrix Inequalities ◽  
Numerical Examples ◽  
Upper Solution ◽  
Solution Bound ◽  
Semidefinite Matrix ◽  
Two Parameter

The continuous coupled algebraic Riccati equation (CCARE) has wide applications in control theory and linear systems. In this paper, by a constructed positive semidefinite matrix, matrix inequalities, and matrix eigenvalue inequalities, we propose a new two-parameter-type upper solution bound of the CCARE. Next, we present an iterative algorithm for finding the tighter upper solution bound of CCARE, prove its boundedness, and analyse its monotonicity and convergence. Finally, corresponding numerical examples are given to illustrate the superiority and effectiveness of the derived results.

scholarly journals A New Algorithm for Positive Semidefinite Matrix Completion

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Fangfang Xu ◽  
Peng Pan
Keyword(s):  
Matrix Completion ◽  
Positive Semidefinite ◽  
Alternating Direction Method ◽  
Low Rank ◽  
System Control ◽  
Positive Semidefinite Matrix ◽  
Linear Least Squares ◽  
Alternating Direction ◽  
Semidefinite Matrix Completion ◽  
Semidefinite Matrix

Positive semidefinite matrix completion (PSDMC) aims to recover positive semidefinite and low-rank matrices from a subset of entries of a matrix. It is widely applicable in many fields, such as statistic analysis and system control. This task can be conducted by solving the nuclear norm regularized linear least squares model with positive semidefinite constraints. We apply the widely used alternating direction method of multipliers to solve the model and get a novel algorithm. The applicability and efficiency of the new algorithm are demonstrated in numerical experiments. Recovery results show that our algorithm is helpful.

A Classification of Spatial Stiffness Based on the Degree of Translational–Rotational Coupling

1999 ◽  
Vol 123 (3) ◽  
pp. 353-358 ◽  
Author(s):  
Shuguang Huang ◽  
Joseph M. Schimmels
Keyword(s):  
Stiffness Matrix ◽  
Full Rank ◽  
Rotational Behavior ◽  
Topological Properties ◽  
Number Of Components ◽  
Stiffness Matrices ◽  
Minimum Number ◽  
Rotational Coupling

Previously, we have shown that, to realize an arbitrary spatial stiffness matrix, spring components that couple the translational and rotational behavior along/about an axis are required. We showed that, three such coupled components and three uncoupled components are sufficient to realize any full-rank spatial stiffness matrix and that, for some spatial stiffness matrices, three coupled components are necessary. In this paper, we show how to identify the minimum number of components that provide the translational-rotational coupling required to realize an arbitrarily specified spatial stiffness matrix. We establish a classification of spatial stiffness matrices based on this number which we refer to as the “degree of translational–rotational coupling” (DTRC). We show that the DTRC of a stiffness matrix is uniquely determined by the spatial stiffness mapping and is obtained by evaluating the eigenstiffnesses of the spatial stiffness matrix. The topological properties of each class are identified. In addition, the relationships between the DTRC and other properties identified in previous investigations of spatial stiffness behavior are discussed.

scholarly journals The Solvency II Standard Formula, Linear Geometry, and Diversification

2016 ◽  
Author(s):  
Joachim Paulusch
Keyword(s):  
Convex Cone ◽  
Risk Measure ◽  
Positive Semidefinite ◽  
Solvency Ii ◽  
Positive Semidefinite Matrix ◽  
Lp Norm ◽  
Standard Formula ◽  
Risk Aggregation ◽  
Semidefinite Matrix ◽  
Linear Geometry

We introduce the notions of monotony, subadditivity, and homogeneity for functions defined on a convex cone, call functions with these properties diversification functions and obtain the respective properties for the risk aggregation given by such a function. Examples of diversification functions are given by seminorms, which are monotone on the convex cone of non-negative vectors. Any Lp norm has this property, and any scalar product given by a non-negative positive semidefinite matrix as well. In particular, the Standard Formula is a diversification function, hence a risk measure that preserves homogeneity, subadditivity, and convexity.

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