scholarly journals A Note on a Lower Bound on the Minimum Rank of a Positive Semidefinite Hankel Matrix Rank Minimization Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yi Xu ◽  
Xiaorong Ren ◽  
Xihong Yan
Keyword(s):  
Lower Bound ◽  
Minimization Problem ◽  
Optimization Problem ◽  
Global Minimum ◽  
Hankel Matrix ◽  
Positive Semidefinite ◽  
Linear Constraints ◽  
Matrix Rank ◽  
Dual Theorem ◽  
Infinite Programming

This paper investigates the problem of approximating the global minimum of a positive semidefinite Hankel matrix minimization problem with linear constraints. We provide a lower bound on the objective of minimizing the rank of the Hankel matrix in the problem based on conclusions from nonnegative polynomials, semi-infinite programming, and the dual theorem. We prove that the lower bound is almost half of the number of linear constraints of the optimization problem.

scholarly journals A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jianchao Bai ◽  
Xuefeng Duan ◽  
Kexin Cheng ◽  
Xuewei Zhang
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problem ◽  
Hankel Matrix ◽  
Positive Semidefinite ◽  
Gradient Algorithm ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Unconstrained Optimization Problem ◽  
Nonlinear Conjugate Gradient ◽  
Rank Approximation

We consider the weighted low rank approximation of the positive semidefinite Hankel matrix problem arising in signal processing. By using the Vandermonde representation, we firstly transform the problem into an unconstrained optimization problem and then use the nonlinear conjugate gradient algorithm with the Armijo line search to solve the equivalent unconstrained optimization problem. Numerical examples illustrate that the new method is feasible and effective.

Practical Tools for Reasoning About Linear Constraints

1991 ◽  
Vol 15 (3-4) ◽  
pp. 357-379
Author(s):  
Tien Huynh ◽  
Leo Joskowicz ◽  
Catherine Lassez ◽  
Jean-Louis Lassez
Keyword(s):  
Logic Programming ◽  
Intelligent Systems ◽  
Optimization Problem ◽  
Spatial Reasoning ◽  
Quantifier Elimination ◽  
Canonical Representation ◽  
Linear Constraints ◽  
Practical Applications ◽  
Variable Elimination ◽  
Fourier Algorithm

We address the problem of building intelligent systems to reason about linear arithmetic constraints. We develop, along the lines of Logic Programming, a unifying framework based on the concept of Parametric Queries and a quasi-dual generalization of the classical Linear Programming optimization problem. Variable (quantifier) elimination is the key underlying operation which provides an oracle to answer all queries and plays a role similar to Resolution in Logic Programming. We discuss three methods for variable elimination, compare their feasibility, and establish their applicability. We then address practical issues of solvability and canonical representation, as well as dynamical updates and feedback. In particular, we show how the quasi-dual formulation can be used to achieve the discriminating characteristics of the classical Fourier algorithm regarding solvability, detection of implicit equalities and, in case of unsolvability, the detection of minimal unsolvable subsets. We illustrate the relevance of our approach with examples from the domain of spatial reasoning and demonstrate its viability with empirical results from two practical applications: computation of canonical forms and convex hull construction.

A Dynamics-Based Optimal Trajectory Generation for Controlling an Automated Excavator

2010 ◽  
Vol 224 (10) ◽  
pp. 2109-2119 ◽  
Author(s):  
S Yoo ◽  
C-G Park ◽  
S-H You ◽  
B Lim
Keyword(s):  
Optimal Trajectory ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Constraints ◽  
Control Points ◽  
Weighting Functions ◽  
B Splines ◽  
Finite Dimensional ◽  
Motion Optimization ◽  
Save Energy

This article presents a new methodology to generate optimal trajectories in controlling an automated excavator. By parameterizing all the actuator displacements with B-splines of the same order and with the same number of control points, the coupled actuator limits, associated with the maximum pump flowrate, are described as the finite-dimensional set of linear constraints to the motion optimization problem. Several weighting functions are introduced on the generalized actuator torque so that the solution to each optimization problems contains the physical meaning. Numerical results showing that the generated motions of the excavator are fairly smooth and effectively save energy, which can prevent mechanical wearing and possibly save fuel consumption, are presented. A typical operator's manoeuvre from experiments is referred to bring out the standing features of the optimized motion.

scholarly journals Hankel Matrix Rank as Indicator of Ghost in Bearing-Only Tracking

2018 ◽  
Vol 54 (6) ◽  
pp. 2713-2723 ◽  
Author(s):  
Korkut Bekiroglu ◽  
Mustafa Ayazoglu ◽  
Constantino Lagoa ◽  
Mario Sznaier
Keyword(s):  
Hankel Matrix ◽  
Matrix Rank

Solution to time-energy costs of quantum channels

2015 ◽  
Vol 15 (7&8) ◽  
pp. 685-693
Author(s):  
Chi-Hang F. Fung ◽  
H. F. Chau ◽  
Chi-Kwong Li ◽  
Nung-Sing Sze
Keyword(s):  
Lower Bound ◽  
Semidefinite Programming ◽  
Energy Cost ◽  
Quantum Channel ◽  
Positive Semidefinite ◽  
Energy Costs ◽  
Quantum Channels ◽  
General Quantum ◽  
Special Cases

We derive a formula for the time-energy costs of general quantum channels proposed in [Phys. Rev. A {\bf 88}, 012307 (2013)]. This formula allows us to numerically find the time-energy cost of any quantum channel using positive semidefinite programming. We also derive a lower bound to the time-energy cost for any channels and the exact the time-energy cost for a class of channels which includes the qudit depolarizing channels and projector channels as special cases.

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