Modified Exact Jacobian Semidefinite Programming Relaxation for Celis-Dennis-Tapia Problem

A modified exact Jacobian semidefinite programming(SDP)relaxation method is proposed in this paper to solve the Celis-Dennis-Tapia(CDT)problem using the Jacobian matrix of objective and constraining polynomials.In the modified relaxation problem,the number of introduced constraints and the lowest relaxation order decreases significantly.At the same time,the finite convergence property is guaranteed.In addition,the proposed method can be applied to the quadratically constrained problem with two quadratic constraints.Moreover,the efficiency of the proposed method is verified by numerical experiments.

Minor Self-conjugate and Skewpositive Semidefinite Solutions to a System of Matrix Equations over Skew Fields

Minor self conjugate (msc) and skewpositive semidefinite (ssd) solutions to the system of matrix equations over skew fields [A mn X nn =A mn ,B sn X nn =O sn ] are considered. Necessary and sufficient conditions for the existence of and the expressions for the msc solutions and the ssd solutions are obtained for the system.

A class of polynomial primal-dual interior-point algorithms for semidefinite optimization

In the present paper we present a class of polynomial primal-dual interior-point algorithms for semidefmite optimization based on a kernel function. This kernel function is not a so-called self-regular function due to its growth term increasing linearly. Some new analysis tools were developed which can be used to deal with complexity ＂analysis of the algorithms which use analogous strategy in [5] to design the search directions for the Newton system. The complexity bounds for the algorithms with large- and small-update methodswere obtained, namely,O（qn^（p＋q/q（P＋1）log n/ε and O（q^2√n）log n/ε,respectlvely.

Complexity analysis of interior-point algorithm based on a new kernel function for semidefinite optimization

Interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with simple algebraic expression is proposed. Based on this kernel function, a primal-dual interior-point methods （IPMs） for semidefinite optimization （SDO） is designed. And the iteration complexity of the algorithm as O（n^3/4 log n/ε） with large-updates is established. The resulting bound is better than the classical kernel function, with its iteration complexity O（n log n/ε） in large-updates case.

A GLOBAL LINEAR AND LOCAL QUADRATIC SINGLE-STEP NONINTERIOR CONTINUATION METHOD FOR MONOTONE SEMIDEFINITE COMPLEMENTARITY PROBLEMS

A noninterior continuation method is proposed for semidefinite complementarity problem （SDCP）. This method improves the noninterior continuation methods recently developed for SDCP by Chen and Tseng. The main properties of our method are： （i） it is well d.efined for the monotones SDCP; （ii） it has to solve just one linear system of equations at each step; （iii） it is shown to be both globally linearly convergent and locally quadratically convergent under suitable assumptions.

PROBLEM OF EQUALITIES IN EIGENVALUE INEQUALITIES FOR PRODUCTS OF POSITIVE SEMIDEFINITE HERMITIAN MATRICES

Let A∈Cm×n,set eigenvalues of matrix A with |λ1 （A）|≥|λ2（A）|≥…≥|λn（A）|,write A≥0 if A is a positive semidefinite Hermitian matrix, and denote∧k （A）=diag （λ1（A）,…,λk（A））,∧(（n-k）.（A）=diag (λk+1（A）,…,λn（A）)for any k=1, 2,...,n if A≥0. Denote all n order unitary matrices by Un×n.Problem of equalities to hold in eigenvalue inequalities for products of matrices

Generalized bipositive semidefinite solutions to a system of matrix equations

In this paper, a system of complex matrix equations was studied. Necessary and sufficient conditions for the existence and the expression of generalized bipositive semidefinite solution to the system were given. In addition, a criterion for a matrix to be generalized bipositive semidefinite was determined.

Semidefinite programming approach for TDOA/GROA based source localization

Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.

Some Properties of Solution to Semidefinite Complementarity Problem

In this paper, we discuss the nonemptyness and boundedness of the solution set for P*-semidefinite complementarity problem by using the concept of exceptional family of elements for complementarity problems over the cone of semidefinite matrices, and obtain a main result that if the corresponding problem has a strict feasible point, then its solution set is nonemptyness and boundedness.

Safe Bounds in Semidefinite Programming by Using Interval Arithmetic

Efficient solvers for optimization problems are based on linear and semidefinite relaxations that use floating point arithmetic. However, due to the rounding errors, relaxation thus may overestimate, or worst, underestimate the very global optima. The purpose of this article is to introduce an efficient and safe procedure to rigorously bound the global optima of semidefinite program. This work shows how, using interval arithmetic, rigorous error bounds for the optimal value can be computed by carefully post processing the output of a semidefinite programming solver. A lower bound is computed on a semidefinite relaxation of the constraint system and the objective function. Numerical results are presented using the SDPA (SemiDefinite Programming Algorithm), solver to compute the solution of semidefinite programs. This rigorous bound is injected in a branch and bound algorithm to solve the optimisation problem.

TWO-STAGE MULTISPLITTING OF SYMMETRIC POSITIVE SEMIDEFINITE MATRICES

Main resultsTheorem 1 Let A be symmetric positive semidefinite.Let (?) be a diagonally compen-sated reduced matrix of A and Let (?)=σI+(?)(σ】0) be a modiffication(Stieltjes) matrixof (?).Let the splitting (?)=M-(?) be regular and M=F-G be weak regular,where M andF are symmetric positive definite matrices.Then the resulting two-stage method corre-sponding to the diagonally compensated reduced splitting A=M-N and inner splitting M=F-G is convergent for any number μ≥1 of inner iterations.Furthermore,the

Realizing complex beams via amplitude-phase digital coding metasurfaces and semidefinite relaxation optimization

Complex beams play important roles in wireless communications,radar,and satellites,and have attracted great interest in recent years.In light of this background,we present a fast and efficient approach to realize complex beams by using semidefinite relaxation(SDR)optimization and amplitude-phase digital coding metasurfaces.As the application examples of this approach,complex beam patterns with cosecant,flat-top,and double shapes are designed and verified using full-wave simulations and experimental measurements.The results show excellent main lobes and low-level side lobes and demonstrate the effectiveness of the approach.Compared with previous works,this approach can solve the complex beam-forming problem more rapidly and effectively.Therefore,the approach will be of great significance in the design of beam-forming systems in wireless applications.

Weighted semidefinite programming scheme for wireless positioning with lognormal shadowing

Received signal strength(RSS)based positioning schemes ignore the actual environmental feature that the volatility of RSS increases as signal propagation distance grows.Therefore,RSS over long distance generally has relatively large measurement error and degrades the positioning performance.To reduce the negative impact of these RSSs over long distances,a weighted semidefinite programming(WSDP)positioning scheme was proposed.The WSDP positioning scheme first assesses the signal propagation quality using the average variance of all RSS sets.Then appropriate weighting factors are set based on the variance of each RSS set,and a weighted semidefinite programming optimizer is formulated to estimate the positions of target nodes.Simulation results show that the WSDP positioning scheme can effectively improve the positioning performance.

Optimal Micro-PMU Placement for Improving State Estimation Accuracy via Mixed-integer Semidefinite Programming

Micro-phasor measurement units(μPMUs)with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations.Therefore,this paper investigates the problem of how to place a limited number ofμPMUs to improve the state estimation accuracy.Combined with pseudo-measurements and supervisory control and data acquisition(SCADA)measurements,an optimalμPMU placement model is proposed based on a two-step state estimation method.The E-optimal experimental criterion is utilized to measure the state estimation accuracy.The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming(MISDP)problem,whose optimal solution can be obtained by using the improved Benders decomposition method.Simulations on several systems are carried out to evaluate the effective performance of the proposed model.

Joint Beamforming Design for Dual-Functional Radar-Communication Systems Under Beampattern Gain Constraints

The joint beamforming design challenge for dual-functional radar-communication systems is addressed in this paper.The base station in these systems is tasked with simultaneously sending shared signals for both multi-user communication and target sensing.The primary objective is to maximize the sum rate of multi-user communication,while also ensuring sufficient beampattern gain at particular angles that are of interest for sensing,all within the constraints of the transmit power budget.To tackle this complex non-convex problem,an effective algorithm that iteratively optimizes the joint beamformers is developed.This algorithm leverages the techniques of fractional programming and semidefinite relaxation to achieve its goals.The numerical results confirm the effectiveness of the proposed algorithm.

AN EQUIVALENCY CONDITION OF NONSINGULARITY IN NONLINEAR SEMIDEFINITE PROGRAMMING

In this paper,an equivalency condition of nonsingularity in nonlinear semidefinite programming,which can be viewed as a generalization of the equivalency condition of nonsingularity for linearsemidefinite programming,is established under certain conditions of convexity.

On filter-successive linearization methods for nonlinear semidefinite programming

In this paper we present a filter-successive linearization method with trust region for solutions of nonlinear semidefinite programming. Such a method is based on the concept of filter for nonlinear programming introduced by Fletcher and Leyffer in 2002. We describe the new algorithm and prove its global convergence under weaker assumptions. Some numerical results are reported and show that the new method is potentially efficient.

LEAST-SQUARES SOLUTION OF AXB = DOVER SYMMETRIC POSITIVE SEMIDEFINITE MATRICES X

Least-squares solution of AXB = D with respect to symmetric positive semidefinite matrix X is considered. By making use of the generalized singular value decomposition, we derive general analytic formulas, and present necessary and sufficient conditions for guaranteeing the existence of the solution. By applying MATLAB 5.2, we give some numerical examples to show the feasibility and accuracy of this construction technique in the finite precision arithmetic.

CHARACTERISTIC FINITE DIFFERENCE METHODS FOR POSITIVE SEMIDEFINITE PROBLEM OF TWO-PHASE(OIL AND WATER)MISCIBLE FLOW IN POROUS MEDIA

Numerical simulation of two-phase (oil and water) miscible flow in porousmedia is the mathematical foundation in energy problems. For a two-dimensional posi-tive problem, Douglas put forward the well-known characteristic finite difference method.However, for numerical analysis there exist difficulties. They assumed that the problem isperiodic and the diffusion matrix of the concentration equation was positive definite. Butin many practical situations the diffusion matrixes are only positive semidefinite. In thispaper, we put forward a kind of characteristic finite difference schemes and obtain optimalorder estimates in l2 norm for the error in the approximation assumptions.

A Filter Method for Nonlinear Semidefinite Programming with Global Convergence

In this study, a new filter algorithm is presented for solving the nonlinear semidefinite programming. This algorithm is inspired by the classical sequential quadratic programming method. Unlike the traditional filter methods, the sufficient descent is ensured by changing the step size instead of the trust region radius. Under some suitable conditions, the global convergence is obtained. In the end, some numerical experiments are given to show that the algorithm is effective.