An efficient algorithm for the projection of a point on the intersection of two hyperplanes and a box in $$\mathbb {R}^n$$

EURO Journal on Computational Optimization - Tập 7 - Trang 177-207 - 2018
Cláudio P. Santiago1, Sérgio Assunção Monteiro2, Helder Inácio3, Nelson Maculan2, Maria Helena Jardim4
1Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, USA
2COPPE - Systems Engineering and Computer Science, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
3School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA
4Department of Computer Science, Institute of Mathematics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Tóm tắt

In this work, we present an efficient strongly polynomial algorithm for the projection of a point on the intersection of two hyperplanes and a box in $$\mathbb {R}^n$$ . Interior point methods are the most efficient algorithm in the literature to solve this problem. While efficient in practice, the complexity of interior-point methods is bounded by a polynomial in the dimension of the problem and in the accuracy of the solution. Moreover, their efficiency is highly dependent on a series of parameters depending on the specific method chosen (especially for nonlinear problems), such as step size, barrier parameter, accuracy, among others. We propose a new method based on the KKT optimality conditions. In this method, we write the problem as a function of the Lagrangian multipliers of the hyperplanes and seek to find the pair of multipliers that corresponds to the optimal solution. We prove that the algorithm has complexity $$O(n^2 \log n)$$ .

Tài liệu tham khảo

Athans M, Falb PL (1966) Optimal control. McGraw-Hill, New York Ben-Tal A, Nemirovski A (2001) Lectures on modern convex optimization: analysis, algorithms, and engineering applications. MPS-SIAM series on optimization. SIAM, Philadelphia Brucker P (1984) An \({O} (n)\) algorithm for quadratic knapsack problems. Oper Res Lett 3(3):163–166 Byrd RH, Peihuang L, Nocedal J, Zhu C (1995) A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput 16(5):1190–1208 Kiwiel KC (2008) Breakpoint searching algorithms for the continuous quadratic knapsack problem. Math Programm 112:473–491 Koza JR (1992) Genetic programming. MIT Press, Cambridge Lin C-J, Moré JJ (1998) Newton’s method for large bound-constrained optimization problems. SIAM J Optim 9:1100–1127 Maculan N, Minoux M, Plateau G (1997) A \(\mathit{O}(n)\) algorithm for projecting a vector on the intersection of a hyperplane and \({\mathbb{R}}^n_+\). RAIRO Oper Res 3(1):7–16 Maculan N, Santiago CP, Macambira EM, Jardim MHC (2003) An \(\mathit{O}(n)\) algorithm for projecting a vector on the intersection of a hyperplane and a box in \({\mathbb{R}}^n\). J Optim Theory Appl 117(3):553–574 Maculan Nelson, de Paula Geraldo Galdino (1989) A linear-time median-finding algorithm for projecting a vector on the simplex of \(\mathbb{R}^n\). Oper Res Lett 8(4):219–222 Michelot Christian (1986) A finite algorithm for finding the projection of a point onto the canonical simplex of \(\mathbb{R}^n\). J Optim Theory Appl 50(1):195–200 Morales José Luis, Nocedal Jorge (2011) Remark on algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound constrained optimization. ACM Trans Math Softw 38(1):7:1–7:4 Pardalos Panos M, Kovoor Naina (1990) An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds. Math Programm 46(1):321–328 Wright Stephen J (1987) Primal–dual interior point methods. SIAM, philadelphia