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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：An active contour model with local variance force term and its efficient minimization solver for multi-phase image
局部方差力项 活动轮廓模型 多相图像 最小化求解器
2023/5/8
In this work, we propose an active contour model with a local variance force (LVF) term that can be applied to multi-phase image segmentation problems. With the LVF, the proposed model is very effecti...
A rank minimization heuristic with application to minimum order system approximation
Variable control system analysis and controller synthesis matrix rank linear matrix inequality (lmi) the positive semi-definite matrix variables
2015/8/11
Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) c...
Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices
Positive semi-definite matrix convex sets functions half positive definite matrix distance data
2015/8/11
We present a heuristic for minimizing the rank of a positive semidefinite matrix over a convex set. We use the logarithm of the determinant as a smooth approximation for rank, and locally minimize thi...
Rank minimization and applications in system theory
Matrix the convex set system identification statistics signal processing
2015/8/11
In this tutorial paper, we consider the problem of minimizing the rank of a matrix over a convex set. The Rank Minimization Problem (RMP) arises in diverse areas such as control, system identification...
Solving interpolation problems via generalized eigenvalue minimization
Solving interpolation problems generalized eigenvalue minimization
2015/7/13
A number of problems in the analysis and design of control systems may be reformulated as the problem of minimizing the largest generalized eigenvalue of a pair of symmetric matrices which depend affi...
A Rank Minimization Heuristic with Application to Minimum Order System Approximation
Rank Minimization Heuristic Minimum Order System Approximation
2015/7/10
Several problems arising in control system analysis and design, such as reduced order controller synthesis, involve minimizing the rank of a matrix variable subject to linear matrix inequality (LMI) c...
Log-Det Heuristic for Matrix Rank Minimization with Applications to Hankel and Euclidean Distance Matrices
Log-Det Heuristic Matrix Rank Minimization Applications Hankel Euclidean Distance Matrices
2015/7/10
We present a heuristic for minimizing the rank of a positive semidefinite matrix over a convex set. We use the logarithm of the determinant as a smooth approximation for rank, and locally minimize thi...
Rank Minimization and Applications in System Theory
Rank Minimization Applications System Theory
2015/7/10
In this tutorial paper, we consider the problem of minimizing the rank of a matrix over a convex set. The Rank Minimization Problem (RMP) arises in diverse areas such as control, system identification...
Enhancing Sparsity by Reweighted l1 Minimization
Iterative reweighting Underdetermined systems of linear equations Compressive sensing Dantzig selector Sparsity
2015/7/9
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
A Comparison of Cross-Entropy and Variance Minimization Strategies
variance minimization cross-entropy importance sampling rareevent simulation likelihood ratio degeneracy
2015/7/6
The variance minimization (VM) and cross-entropy (CE) methods are two versatile adaptive importance sampling procedures that have been successfully applied to a wide variety of difficult rare-event es...
Cross-layer Energy Minimization in TDMA-based Sensor Networks
TDMA-based Sensor Networks Minimization
2015/6/19
We consider sensor networks where energy is a limited resource so that energy consumption must be minimized while satisfying given throughput requirements. Moreover, energy consumption must take into ...
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Dual Coordinate Ascent Methods Regularized Loss Minimization
2012/11/22
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel...
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Stochastic Dual Coordinate Ascent Methods Regularized Loss Minimization
2012/11/22
Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closel...
Fast and Accurate Algorithms for Re-Weighted L1-Norm Minimization
Fast and Accurate Algorithms Re-Weighted L1-Norm Minimization
2012/9/17
To recover a sparse signal from an underdetermined system, we often solve a constrained`1-norm minimization problem. In many cases, the signal sparsity and the recovery performance can be further impr...
Non-Convex Rank Minimization via an Empirical Bayesian Approach
Non-Convex Rank Minimization via Empirical Bayesian Approach
2012/9/19
In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem.Consequently, the convex nuclear ...