Complex Mode Frequency Iteration Method for Flutter Analysis of 2-DOF Systems

For a vibration system with 2 DOF of bend and torsion, its critical flutter wind speed can be calculated by using complex mode frequency iteration (CMFI) method based on MatLab 5.2, the results of which are in agree with those acquired by wind tunnel test. Not only critical flutter wind speed, but also vibration characteristic of a system under different wind speeds can be determined. CMFI method is suitable for both of separated flow torsional flutter and classic coupling flutter analysis, which is presented by flutter analysis of an ideal thin plate and a bluff bridge deck. Furthermore, it is proved through the investigation of the relationship between flutter derivatives and its critical flutter wind speed that coupling aerodynamic derivatives are necessary for classic coupling flutter to occur.]

A Full-Newton Step Feasible Interior-Point Algorithm for the Special Weighted Linear Complementarity Problems Based on a Kernel Function

In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.

First-Order Algorithms for Convex Optimization with Nonseparable Objective and Coupled Constraints

In this paper,we consider a block-structured convex optimization model,where in the objective the block variables are nonseparable and they are further linearly coupled in the constraint.For the 2-block case,we propose a number of first-order algorithms to solve this model.First,the alternating direction method of multipliers(ADMM)is extended,assuming that it is easy to optimize the augmented Lagrangian function with one block of variables at each time while fixing the other block.We prove that O(1/t)iteration complexity bound holds under suitable conditions,where t is the number of iterations.If the subroutines of the ADMM cannot be implemented,then we propose new alternative algorithms to be called alternating proximal gradient method of multipliers,alternating gradient projection method of multipliers,and the hybrids thereof.Under suitable conditions,the O(1/t)iteration complexity bound is shown to hold for all the newly proposed algorithms.Finally,we extend the analysis for the ADMM to the general multi-block case.

Randomized Primal–Dual Proximal Block Coordinate Updates

In this paper,we propose a randomized primal–dual proximal block coordinate updating framework for a general multi-block convex optimization model with coupled objective function and linear constraints.Assuming mere convexity,we establish its O(1/t)convergence rate in terms of the objective value and feasibility measure.The framework includes several existing algorithms as special cases such as a primal–dual method for bilinear saddle-point problems(PD-S),the proximal Jacobian alternating direction method of multipliers(Prox-JADMM)and a randomized variant of the ADMM for multi-block convex optimization.Our analysis recovers and/or strengthens the convergence properties of several existing algorithms.For example,for PD-S our result leads to the same order of convergence rate without the previously assumed boundedness condition on the constraint sets,and for Prox-JADMM the new result provides convergence rate in terms of the objective value and the feasibility violation.It is well known that the original ADMM may fail to converge when the number of blocks exceeds two.Our result shows that if an appropriate randomization procedure is invoked to select the updating blocks,then a sublinear rate of convergence in expectation can be guaranteed for multi-block ADMM,without assuming any strong convexity.The new approach is also extended to solve problems where only a stochastic approximation of the subgradient of the objective is available,and we establish an O(1/√t)convergence rate of the extended approach for solving stochastic programming.

A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term

In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determining the search directions and for measuring the distance between the given iterate and theμ-center for the algorithm.By developing a new norm-based proximity measure and some technical results,we derive the iteration bound that coincides with the currently best known iteration bound for the algorithm with small-update method.In our knowledge,this result is the first instance of full-Newton step feasible interior-point method for SDO which involving the kernel function.

On the Convergence Rate of a Class of Proximal-Based Decomposition Methods for Monotone Variational Inequalities

A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012,while only global convergence is proved at the same time.In this paper,we give a unified proof on theO(1/t)iteration complexity,together with the linear convergence rate for this kind of proximal-based decomposition methods.Besides theε-optimal iteration complexity result defined by variational inequality,the non-ergodic relative error of adjacent iteration points is also proved to decrease in the same order.Further,the linear convergence rate of this algorithm framework can be constructed based on some special variational inequality properties,without necessary strong monotone conditions.

Fixed-point ICA algorithm for blind separation of complex mixtures containing both circular and noncircular sources

Fixed-point algorithms are widely used for independent component analysis（ICA） owing to its good convergence. However, most existing complex fixed-point ICA algorithms are limited to the case of circular sources and result in phase ambiguity, that restrict the practical applications of ICA. To solve these problems, this paper proposes a two-stage fixed-point ICA（TS-FPICA） algorithm which considers complex signal model. In this algorithm, the complex signal model is converted into a new real signal model by utilizing the circular coefficients contained in the pseudo-covariance matrix. The algorithm is thus valid to noncircular sources. Moreover, the ICA problem under the new model is formulated as a constrained optimization problem, and the real fixed-point iteration is employed to solve it. In this way, the phase ambiguity resulted by the complex ICA is avoided. The computational complexity and convergence property of TS-FPICA are both analyzed theoretically. Simulation results show that the proposed algorithm has better separation performance and without phase ambiguity in separated signals compared with other algorithms. TS-FPICA convergences nearly fast as the other fixed-point algorithms, but far faster than the joint diagonalization method, e.g. joint approximate diagonalization of eigenmatrices（JADE）.