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We study the problem of asset and liability management of participating insurance policies with guarantees. We develop a scenario optimization model for integrative asset and liability management, analyze the tradeoffs in structuring such policies, and study alternative choices in funding them. The nonlinearly constrained optimization model can be linearized through closed form solutions of the dynamic equations. Thus large-scale problems are solved with standard methods. We report on an empirical analysis of policies offered by Italian insurers. The optimized model results are in general agreement with current industry practices. However, some inefficiencies are identified and potential improvements are highlighted. 相似文献
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Gauss—Seidel type relaxation techniques are applied in the context of strictly convex pure networks with separable cost functions. The algorithm is an extension of the Bertsekas—Tseng approach for solving the linear network problem and its dual as a pair of monotropic programming problems. The method is extended to cover the class of generalized network problems. Alternative internal tactics for the dual problem are examined. Computational experiments — aimed at the improved efficiency of the algorithm — are presented.This research was supported in part by National Science Foundation Grant No. DCR-8401098-A01. 相似文献
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A Scalable Parallel Interior Point Algorithm for Stochastic Linear Programming and Robust Optimization 总被引:1,自引:0,他引:1
We present a computationally efficient implementation of an interior point algorithm for solving large-scale problems arising in stochastic linear programming and robust optimization. A matrix factorization procedure is employed that exploits the structure of the constraint matrix, and it is implemented on parallel computers. The implementation is perfectly scalable. Extensive computational results are reported for a library of standard test problems from stochastic linear programming, and also for robust optimization formulations.The results show that the codes are efficient and stable for problems with thousands of scenarios. Test problems with 130 thousand scenarios, and a deterministic equivalent linear programming formulation with 2.6 million constraints and 18.2 million variables, are solved successfully. 相似文献
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The original proximal minimization algorithm employs quadratic additive terms in the objectives of the subproblems. In this paper, we replace these quadratic additive terms by more generalD-functions which resemble (but are not strictly) distance functions. We characterize the properties of suchD-functions which, when used in the proximal minimization algorithm, preserve its overall convergence. The quadratic case as well as an entropy-oriented proximal minimization algorithm are obtained as special cases.The work of the first author was supported by NSF Grant CCR-8811135 and NIH Grant HL-28438, while visiting the Decision Sciences Department of the Wharton School and the Medical Image Processing Group at the Department of Radiology, both at the University of Pennsylvania, Philadelphia, Pennsylvania. The work of the second author was supported by NSF Grant CCR-91-04042 and AFOSR Grant 91-0168.The authors received valuable comments on the December 1989 and June 1990 versions of this paper, which led to considerable improvements of the results and their presentation. For this, they are indebted to D. Bertsekas, A. De Pierro, J. Eckstein, P. Eggermont, A. Iusem, M. Ferris, M. Teboulle, and three anonymous referees. 相似文献
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Integrated simulation and optimization models for tracking international fixed income indices 总被引:1,自引:0,他引:1
Portfolio managers in the international fixed income markets must address jointly the interest rate risk in each market and
the exchange rate volatility across markets. This paper develops integrated simulation and optimization models that address
these issues in a common framework. Monte Carlo simulation procedures generate jointly scenarios of interest and exchange
rates and, thereby, scenarios of holding period returns of the available securities. The portfolio manager’s risk tolerance is incorporated either through a utility function or
a (modified) mean absolute deviation function. The optimization models prescribe asset allocation weights among the different
markets and also resolve bond-picking decisions. Therefore several interrelated decisions are cast in a common framework.
Two models – an expected utility maximization and a mean absolute deviation minimization – are implemented and tested empirically
in tracking a composite index of the international bond markets. Backtesting over the period January 1997 to July 1998 illustrate
the efficacy of the optimization models in dealing with uncertainty and tracking effectively the volatile index. Of particular
interest is the empirical demostration that the integrative models generate portfolios that dominate the portfolios obtained
using classical disintegrated approaches.
Received: November 24, 1998 / Accepted: October 1, 2000?Published online December 15, 2000 相似文献
8.
Gauss—Seidel type relaxation techniques are applied in the context of strictly convex pure networks with separable cost functions.
The algorithm is an extension of the Bertsekas—Tseng approach for solving the linear network problem and its dual as a pair
of monotropic programming problems. The method is extended to cover the class of generalized network problems. Alternative
internal tactics for the dual problem are examined. Computational experiments —aimed at the improved efficiency of the algorithm
— are presented.
This research was supported in part by National Science Foundation Grant No. DCR-8401098-A0l. 相似文献
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Stavros A. Zenios 《Computational Optimization and Applications》1994,3(3):199-242
Data level parallelism is a type of parallelism whereby operations are performed on many data elements concurrently, by many processors. These operations are (more or less) identical, and are executed in a synchronous, orderly fashion. This type of parallelism is used by massively parallel SIMD (i.e., Single Instruction, Multiple Data) architectures, like the Connection Machine CM-2, the AMT DAP and Masspar, and MIMD (i.e., Multiple Instruction, Multiple Data) architectures, like the Connection Machine CM-5. Data parallelism can also be described by a theoretical model of computation: the Vector-Random Access Machine (V-RAM).In this paper we discuss practical approaches to the data-parallel solution of large scale optimization problems with network—or embedded-network—structures. The following issues are addressed: (1) The concept of dataparallelism, (2) algorithmic principles that lead to data-parallel decomposition of optimization problems with network—or embedded-network—structures, (3) specific algorithms for several network problems, (4) data-structures needed for efficient implementations of the algorithms, and (5) empirical results that highlight the performance of the algorithms on a data-parallel computer, the Connection Machine CM-2. 相似文献
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Scott A. Malcolm Stavros A. Zenios 《The Journal of the Operational Research Society》1994,45(9):1040-1049
We develop a robust optimization model for planning power system capacity expansion in the face of uncertain power demand. The model generates capacity expansion plans that are both solution and model robust. That is, the optimal solution from the model is ‘almost’ optimal for any realization of the demand scenarios (i.e. solution robustness). Furthermore, the optimal solution has reduced excess capacity for any realization of the scenarios (i.e. model robustness). Experience with a characteristic test problem illustrates not only the unavoidable trade-offs between solution and model robustness, but also the effectiveness of the model in controlling the sensitivity of its solution to the uncertain input data. The experiments also illustrate the differences of robust optimization from the classical stochastic programming formulation. 相似文献