Notion of metrically regular property and certain types of point-based approximations are used for solving the nonsmooth generalized equation f(x)+F(x)?0,where X and Y are Banach spaces,and U is an open subset of X,f:U→Y is a nonsmooth function and F:X■Y is a set-valued mapping with closed graph.We introduce a confined Newton-type method for solving the above nonsmooth generalized equation and analyze the semilocal and local convergence of this method.Specifically,under the point-based approximation of f on U and metrically regular property of f+F,we present quadratic rate of convergence of this method.Furthermore,superlinear rate of convergence of this method is provided under the conditions that f admits p-point-based approximation on U and f+F is metrically regular.An example of nonsmooth functions that have p-point-based approximation is given.Moreover,a numerical experiment is given which illustrates the theoretical result.
In this paper,we discuss a gradient-enhancedℓ_(1)approach for the recovery of sparse Fourier expansions.By gradient-enhanced approaches we mean that the directional derivatives along given vectors are utilized to improve the sparse approximations.We first consider the case where both the function values and the directional derivatives at sampling points are known.We show that,under some mild conditions,the inclusion of the derivatives information can indeed decrease the coherence of measurementmatrix,and thus leads to the improved the sparse recovery conditions of theℓ_(1)minimization.We also consider the case where either the function values or the directional derivatives are known at the sampling points,in which we present a sufficient condition under which the measurement matrix satisfies RIP,provided that the samples are distributed according to the uniform measure.This result shows that the derivatives information plays a similar role as that of the function values.Several numerical examples are presented to support the theoretical statements.Potential applications to function(Hermite-type)interpolations and uncertainty quantification are also discussed.
In this paper we propose an efficient and robust method for computing the analytic center of the polyhedral set P={x€R^n|Ax=b,x>0},where the matrix A€ Rm×n is ill-conditioned,and there are errors in A and b.Besides overcoming the difficulties caused by ill-cond计ioning of the matrix A and errors in A and b,our method can also detect the infeasibility and the unboundedness of the polyhedral set P automatically during the compu tation.Det ailed mat hematical analyses for our method are presen ted and the worst case complexity of the algorithm is also given.Finally some numerical results are presented to show the robustness and effectiveness of the new method.
The pooling problem,also called the blending problem,is fundamental in production planning of petroleum.It can be formulated as an optimization problem similar with the minimum-cost flow problem.However,Alfaki and Haugland(J Glob Optim 56:897–916,2013)proved the strong NP-hardness of the pooling problem in general case.They also pointed out that it was an open problem to determine the computational complexity of the pooling problem with a fixed number of qualities.In this paper,we prove that the pooling problem is still strongly NP-hard even with only one quality.This means the quality is an essential difference between minimum-cost flow problem and the pooling problem.For solving large-scale pooling problems in real applications,we adopt the non-monotone strategy to improve the traditional successive linear programming method.Global convergence of the algorithm is established.The numerical experiments show that the non-monotone strategy is effective to push the algorithm to explore the global minimizer or provide a good local minimizer.Our results for real problems from factories show that the proposed algorithm is competitive to the one embedded in the famous commercial software Aspen PIMS.
