Sensitivity analysis for a Lagrange dual problem to a vector optimization problem

In this paper, sensitivity analysis for a Lagrange dual problem to a vector optimization problem is firstly studied. Then sensitivity analysis of the vector optimization problem is also discussed. Finally, the dual relationships between the obtained results are established.     

Parallel solver for trajectory optimization search directions

A key algorithmic element of a real-time trajectory optimization hardware/software implementation is presented, the search step solver. This is one piece of an algorithm whose overall goal is to make nonlinear trajectory optimization fast enough to provide real-time commands during guidance of a vehicle such as an aeromaneuvering orbiter or the National Aerospace Plane. Many methods of nonlinear programming require the solution of a quadratic program (QP) at each iteration to determine the search step. In the trajectory optimization case, the QP has a special dynamic programming structure, an LQR-like structure. The algorithm exploits this special structure with a divide-and-conquer type of parallel implementation. A hypercube message-passing parallel machine, the INTEL iPSC/2, has been used. The algorithm solves a (p·N)-stage problem onN processors inO(p + log₂ N) operations. The algorithm yields a factor of 8 speed-up over the fastest known serial algorithm when solving a 1024-stage test problem on 32 processors.This research was supported in part by the National Aeronautics and Space Administration under Grant No. NAG-1-1009.     

Use of an orthogonal projector for accelerating a queuing problem solver

Overflow queuing models are often analyzed by explicitly solving a large sparse singular linear systems arising from Kolmogorov balance equations. The system is often converted into an eigenvalue problem the dominant eigenvector of which is the desired null vector. In this paper, we convert an overflow queuing problem into an eigen-value problem of size 1/2 of the original. Then we devise an orthogonal projector that enhances its convergence by removing unwanted eigen-components effectively. Numerical result with some suggestion is given at the end.     

A parallel eigenvalue problem solver for computational nanoelectronics

A new parallel eigenvalue solver for finding the interior eigenvalues of a standard Hermitian eigenvalue problem arising in atomistic simulations in nanoelectronics is presented. It is based on the Tracemin algorithm which finds the p smallest eigenpairs of a generalized Hermitian eigenvalue problem. The original problem is modified using spectrum folding or a quadratic mapping so that the interior eigenvalues are mapped onto the smallest or the largest, respectively. In the latter case the solution of systems in every iteration of Tracemin is avoided and Chebyshev polynomials are used to speedup convergence. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Asynchronously parallel optimization solver for finding multiple minima

We propose and analyze an asynchronously parallel optimization algorithm for finding multiple, high-quality minima of nonlinear optimization problems. Our multistart algorithm considers all previously evaluated points when determining where to start or continue a local optimization run. Theoretical results show that when there are finitely many minima, the algorithm almost surely starts a finite number of local optimization runs and identifies every minimum. The algorithm is applicable to general optimization settings, but our numerical results focus on the case when derivatives are unavailable. In numerical tests, a Python implementation of the algorithm is shown to yield good approximations of many minima (including a global minimum), and this ability is shown to scale well with additional resources. Our implementation’s time to solution is shown also to scale well even when the time to perform the function evaluation is highly variable. An implementation of the algorithm is available in the libEnsemble library at https://github.com/Libensemble/libensemble.     

Computation and analysis for a constrained entropy optimization problem in finance

In [T. Coleman, C. He, Y. Li, Calibrating volatility function bounds for an uncertain volatility model, Journal of Computational Finance (2006) (submitted for publication)], an entropy minimization formulation has been proposed to calibrate an uncertain volatility option pricing model (UVM) from market bid and ask prices. To avoid potential infeasibility due to numerical error, a quadratic penalty function approach is applied. In this paper, we show that the solution to the quadratic penalty problem can be obtained by minimizing an objective function which can be evaluated via solving a Hamilton–Jacobian–Bellman (HJB) equation. We prove that the implicit finite difference solution of this HJB equation converges to its viscosity solution. In addition, we provide computational examples illustrating accuracy of calibration.     

A fast solver for the first biharmonic boundary value problem

Summary This paper provides a fast and storage-saving method for the solution of the first biharmonic boundary value problem (b.v.p.). The b.v.p. is approximated via a special variational finite difference technique suggested earlier by V.G. Korneev. It is shown theoretically that our method produces an approximate solution to the finite difference equations inO(NlnNln^–1) arithmetical operations, whereN is the number of unknowns and (0<<1) denotes the relative accuracy required. The numerical results obtained by our computer code CGMFC decisively substantiate the theoretical estimates given.     

Hadamard well-posedness for a set-valued optimization problem

In this paper, we introduce a kind of Hadamard well-posedness for a set-valued optimization problem. By virtue of a scalarization function, we obtain some relationships between weak ${(\varepsilon, e)}$ -minimizers of the set-valued optimization problem and ${\varepsilon}$ -approximate solutions of a scalar optimization problem. Then, we establish a scalarization theorem of P.K. convergence for sequences of set-valued mappings. Based on these results, we also derive a sufficient condition of Hadamard well-posedness for the set-valued optimization problem.     

Algorithms for a multi-level network optimization problem

In this paper we work on a multi-level network optimization problem that integrates into the same model important aspects of: (i) discrete facility location, (ii) topological network design, and (iii) network dimensioning. Potential applications for the model are discussed, stressing its growing importance. The multi-level network optimization problem treated is defined and a mathematical programming formulation is presented. We make use of a branch-and-bound algorithm based on Lagrangean relaxation lower bounds to introduce some new powerful auxiliary algorithms to exactly solve the problem. We conduct a set of computational experiments that indicate the quality of the proposed approach.     

MFCAV近似Riemann解法器在相容拉氏方法中的熵条件分析

 对基于MFCAV(Multi Fluid Channel on Averaged Volume)近似Riemann解法器的相容拉氏方法的熵条件进行了分析. 结果表明与满足声学形式Riemann解法器的熵不同, 前者只能在每个网格边界左、右两侧网格的熵随时间变化的和保证大于零, 即能保证整体熵增, 但不保证传统意义上的在每个网格中的熵增;而后者不仅保证整体熵增, 而且还满足传统意义上的熵增. 因此MFCAV的熵增相对声学形式解法器而言要弱一些, 由此表明其熵增可能要小些, 使得格式的耗散可能要小些.数值算例也验证了分析的正确性.     

A fast Fourier transform based direct solver for the Helmholtz problem

This article is devoted to the efficient numerical solution of the Helmholtz equation in a two‐ or three‐dimensional (2D or 3D) rectangular domain with an absorbing boundary condition (ABC). The Helmholtz problem is discretized by standard bilinear and trilinear finite elements on an orthogonal mesh yielding a separable system of linear equations. The main key to high performance is to employ the fast Fourier transform (FFT) within a fast direct solver to solve the large separable systems. The computational complexity of the proposed FFT‐based direct solver is $\mathrm{??}(N\log N)$ operations. Numerical results for both 2D and 3D problems are presented confirming the efficiency of the method discussed.     

Z-eigenvalue methods for a global polynomial optimization problem

As a global polynomial optimization problem, the best rank-one approximation to higher order tensors has extensive engineering and statistical applications. Different from traditional optimization solution methods, in this paper, we propose some Z-eigenvalue methods for solving this problem. We first propose a direct Z-eigenvalue method for this problem when the dimension is two. In multidimensional case, by a conventional descent optimization method, we may find a local minimizer of this problem. Then, by using orthogonal transformations, we convert the underlying supersymmetric tensor to a pseudo-canonical form, which has the same E-eigenvalues and some zero entries. Based upon these, we propose a direct orthogonal transformation Z-eigenvalue method for this problem in the case of order three and dimension three. In the case of order three and higher dimension, we propose a heuristic orthogonal transformation Z-eigenvalue method by improving the local minimum with the lower-dimensional Z-eigenvalue methods, and a heuristic cross-hill Z-eigenvalue method by using the two-dimensional Z-eigenvalue method to find more local minimizers. Numerical experiments show that our methods are efficient and promising. This work is supported by the Research Grant Council of Hong Kong and the Natural Science Foundation of China (Grant No. 10771120).     

Sample path optimality for a Markov optimization problem

We study a unichain Markov decision process i.e. a controlled Markov process whose state process under a stationary policy is an ergodic Markov chain. Here the state and action spaces are assumed to be either finite or countable. When the state process is uniformly ergodic and the immediate cost is bounded then a policy that minimizes the long-term expected average cost also has an nth stage sample path cost that with probability one is asymptotically less than the nth stage sample path cost under any other non-optimal stationary policy with a larger expected average cost. This is a strengthening in the Markov model case of the a.s. asymptotically optimal property frequently discussed in the literature.     

Stochastic optimization models for a single-sink transportation problem

In this paper, we study a single-sink transportation problem in which the production capacity of the suppliers and the demand of the single customer are stochastic. Shipments are performed by capacitated vehicles, which have to be booked in advance, before the realization of the production capacity and the demand. Once the production capacity and the demand are revealed, there is an option to cancel some of the booked vehicles against a cancellation fee; if the quantity shipped from the suppliers using the booked vehicles is not enough to satisfy the demand, the residual quantity is purchased from an external company. The problem is to determine the number of vehicles to book in order to minimize the total cost. We formulate a two-stage and a multistage stochastic mixed integer linear programming models to solve this problem and test them on a real case provided by Italcementi, the primary Italian cement producer and the fifth largest cement producer in the world. We test the influence of different scenario-tree structures on the solutions of the problem, as well as sensitivity of the results with respect to the cancellation fee.     

An optimization algorithm for a penalized knapsack problem

We study a variation of the knapsack problem in which each item has a profit, a weight and a penalty; the sum of profits of the selected items minus the largest penalty associated with the selected items must be maximized. We present an ILP formulation and an exact optimization algorithm.     

Stability analysis of a general toeplitz systems solver

We show that a fast algorithm for theQR factorization of a Toeplitz or Hankel matrixA is weakly stable in the sense thatR ^T R is close toA ^T A. Thus, when the algorithm is used to solve the semi-normal equationsR ^TRx=A^T b, we obtain a weakly stable method for the solution of a nonsingular Toeplitz or Hankel linear systemAx=b. The algorithm also applies to the solution of the full-rank Toeplitz or Hankel least squares problem min ||Ax-b||₂.     

Solving the flight perturbation problem with meta heuristics

When there is a perturbation in a carefully constructed aircraft schedule, e.g. an aircraft breakdown, it is important to minimize the negative consequences of this disturbance. Here, a tabu search and a simulated annealing approach to the flight perturbation problem are presented. The heuristics use a tree-search algorithm to find new schedules for the aircraft, and utilize a path relinking strategy to explore paths between structurally different solutions. The computational results indicate that the solution strategies, especially the tabu search, can be successfully used to solve the flight perturbation problem.