Computational Optimization and Applications

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Dissimilarity measures for population-based global optimization algorithms
Computational Optimization and Applications - Tập 45 - Trang 257-281 - 2008
Andrea Cassioli, Marco Locatelli, Fabio Schoen
Very hard optimization problems, i.e., problems with a large number of variables and local minima, have been effectively attacked with algorithms which mix local searches with heuristic procedures in order to widely explore the search space. A Population Based Approach based on a Monotonic Basin Hopping optimization algorithm has turned out to be very effective for this kind of problems. In the re...... hiện toàn bộ
Convergence rate for a Radau hp collocation method applied to constrained optimal control
Computational Optimization and Applications - Tập 74 - Trang 275-314 - 2019
William W. Hager, Hongyan Hou, Subhashree Mohapatra, Anil V. Rao, Xiang-Sheng Wang
For control problems with control constraints, a local convergence rate is established for an hp-method based on collocation at the Radau quadrature points in each mesh interval of the discretization. If the continuous problem has a sufficiently smooth solution and the Hamiltonian satisfies a strong convexity condition, then the discrete problem possesses a local minimizer in a neighborhood of the...... hiện toàn bộ
Kỹ Thuật Tìm Kiếm Ngẫu Nhiên Có Kiểm Soát Kết Hợp Với Khái Niệm Làm Nóng Từ Tính Để Giải Quyết Các Vấn Đề Tối Ưu Toàn Cầu Với Số Nguyên và Số Nguyên Hỗn Hợp Dịch bởi AI
Computational Optimization and Applications - Tập 14 - Trang 103-132 - 1999
C. Mohan, H.T. Nguyen
Trong bài báo này, một thuật toán tính toán, được gọi là thuật toán RST2ANU, đã được phát triển để giải quyết các vấn đề tối ưu toàn cầu với số nguyên và số nguyên hỗn hợp. Thuật toán này chủ yếu dựa trên phương pháp tìm kiếm ngẫu nhiên có kiểm soát ban đầu của Price [22i], kết hợp một tiêu chí chấp nhận kiểu làm nóng giả trong quá trình hoạt động của nó, nhằm cho phép không chỉ các chuyển động đi...... hiện toàn bộ
#tối ưu hóa toàn cầu #tìm kiếm ngẫu nhiên có kiểm soát #làm nóng giả #số nguyên #số nguyên hỗn hợp
A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure
Computational Optimization and Applications - Tập 50 - Trang 379-401 - 2010
Fanwen Meng, Jie Sun, Mark Goh
This paper is concerned with solving single CVaR and mixed CVaR minimization problems. A CHKS-type smoothing sample average approximation (SAA) method is proposed for solving these two problems, which retains the convexity and smoothness of the original problem and is easy to implement. For any fixed smoothing constant ε, this method produces a sequence whose cluster points are weak stationary poi...... hiện toàn bộ
Inertial alternating direction method of multipliers for non-convex non-smooth optimization
Computational Optimization and Applications - - 2022
Le Thi Khanh Hien, Duy Nhat Phan, Nicolas Gillis
Robust portfolio optimization: a conic programming approach
Computational Optimization and Applications - Tập 52 - Trang 463-481 - 2011
Kai Ye, Panos Parpas, Berç Rustem
The Markowitz Mean Variance model (MMV) and its variants are widely used for portfolio selection. The mean and covariance matrix used in the model originate from probability distributions that need to be determined empirically. It is well known that these parameters are notoriously difficult to estimate. In addition, the model is very sensitive to these parameter estimates. As a result, the perfor...... hiện toàn bộ
Global Convergence of a Memory Gradient Method for Unconstrained Optimization
Computational Optimization and Applications - Tập 35 - Trang 325-346 - 2006
Yasushi Narushima, Hiroshi Yabe
Memory gradient methods are used for unconstrained optimization, especially large scale problems. The first idea of memory gradient methods was proposed by Miele and Cantrell (1969) and Cragg and Levy (1969). In this paper, we present a new memory gradient method which generates a descent search direction for the objective function at every iteration. We show that our method converges globally to ...... hiện toàn bộ
An empirical evaluation of a walk-relax-round heuristic for mixed integer convex programs
Computational Optimization and Applications - Tập 60 - Trang 559-585 - 2014
Kuo-Ling Huang, Sanjay Mehrotra
Recently, a walk-and-round heuristic was proposed by Huang and Mehrotra (Comput Optim Appl, 2012) for generating high quality feasible solutions of mixed integer linear programs. This approach uses geometric random walks on a polyhedral set to sample points in this set. It subsequently rounds these random points using a heuristic, such as the feasibility pump. In this paper, the walk-and-round heu...... hiện toàn bộ
IPRSDP: a primal-dual interior-point relaxation algorithm for semidefinite programming
Computational Optimization and Applications - - 2024
Rui-Jin Zhang, Xin-Wei Liu, Yu-Hong Dai
We propose an efficient primal-dual interior-point relaxation algorithm based on a smoothing barrier augmented Lagrangian, called IPRSDP, for solving semidefinite programming problems in this paper. The IPRSDP algorithm has three advantages over classical interior-point methods. Firstly, IPRSDP does not require the iterative points to be positive definite. Consequently, it can easily be combined w...... hiện toàn bộ
Convergence of the reweighted ℓ 1 minimization algorithm for ℓ 2–ℓ p minimization
Computational Optimization and Applications - Tập 59 Số 1-2 - Trang 47-61 - 2014
Xiaojun Chen, Weijun Zhou
Tổng số: 1,401   
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