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81.
从决策有限理性角度,引入行为金融理论于机构投资风险优化系统,对多心理账户条件下机构投资的风险优化设计进行了研究。以Friedman和Savage之谜为释例,对机构投资风险优化中的诸多非标准金融异像进行了解释。以Shefrin和Statman行为证券组合理论为核心,建立了多心理账户条件下机构投资的风险优化模型,为机构投资的风险优化实践提供了一种量化分析工具。 相似文献
82.
一类含有P-Laplacian算子的奇异边值问题解的确切个数 总被引:2,自引:0,他引:2
讨论了一类p-L ap lac ian算子型的奇异边值问题正解的确切个数以及解的性质. 相似文献
83.
广义多目标minmax问题的最优性条件和极大熵方法 总被引:1,自引:0,他引:1
本文讨论了广义多目标minmax问题的最优性条件。利用极大熵逼近函数,研究了广义多目标minmax;问题的逼近问题,在较弱的条件下,证明了由极大熵逼近函数导出的多目标逼近问题的临界点的任一极限点均为原广义多目标minmax问题的临界点。 相似文献
84.
ZhenFengZHANG 《数学学报(英文版)》2005,21(1):155-168
In this paper, we extend a classical result of Hua to arithmetic progressions with large moduli. The result implies the Linnik Theorem on the least prime in an arithmetic progression. 相似文献
85.
86.
Understanding the end state of black hole evaporation, the microscopic origin of black hole entropy, the information loss paradox, and the nature of the singularity arising in gravitational collapse - these are outstanding challenges for any candidate quantum theory of gravity. Recently, a midisuperspace model of quantum gravitational collapse has been solved using a lattice regularization scheme. It is shown that the mass of an eternal black hole follows the Bekenstein spectrum, and a related argument provides a fairly accurate estimate of the entropy. The solution also describes a quantized mass-energy distribution around a central black hole, which in the WKB approximation, is precisely Hawking radiation. The leading quantum gravitational correction makes the spectrum non-thermal, thus providing a plausible resolution of the information loss problem. 相似文献
87.
本文考虑广义C-R组^[1](H)的解f=u iv jw∈C^2的一些性质,提出与之相关的两个边值问题,用积分方程方法和调和函数性质证明了边值问题解的存在唯一性,并写出解的积分表达式. 相似文献
88.
89.
The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study 总被引:1,自引:0,他引:1
Bram Verweij Shabbir Ahmed Anton J. Kleywegt George Nemhauser Alexander Shapiro 《Computational Optimization and Applications》2003,24(2-3):289-333
The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte Carlo simulation. In this technique the expected objective function of the stochastic problem is approximated by a sample average estimate derived from a random sample. The resulting sample average approximating problem is then solved by deterministic optimization techniques. The process is repeated with different samples to obtain candidate solutions along with statistical estimates of their optimality gaps.We present a detailed computational study of the application of the SAA method to solve three classes of stochastic routing problems. These stochastic problems involve an extremely large number of scenarios and first-stage integer variables. For each of the three problem classes, we use decomposition and branch-and-cut to solve the approximating problem within the SAA scheme. Our computational results indicate that the proposed method is successful in solving problems with up to 21694 scenarios to within an estimated 1.0% of optimality. Furthermore, a surprising observation is that the number of optimality cuts required to solve the approximating problem to optimality does not significantly increase with the size of the sample. Therefore, the observed computation times needed to find optimal solutions to the approximating problems grow only linearly with the sample size. As a result, we are able to find provably near-optimal solutions to these difficult stochastic programs using only a moderate amount of computation time. 相似文献
90.
The goal of the paper is to analyse properties of solutions for linear thermoelastic systems of type III in one space variable. Our approach does not use energy methods, it bases on a special diagonalization procedure which is different in different parts of the phase space. This procedure allows to derive explicit representations of solutions. These representations help to prove results for well‐posedness of the Cauchy problem, LP–Lq decay estimates on the conjugate line and results for propagation of singularities. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献