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A fast algorithm for solving large scale mean-variance models by compact factorization of covariance matrices. (English) Zbl 0763.90004
Summary: A fast algorithm for solving large scale MV (mean-variance) portfolio optimization problems is proposed. It is shown that by using $$T$$ independent data representing the rate of return of the assets, the MV model consisting of $$n$$ assets can be put into a quadratic program with $$n+T$$ variables, $$T$$ linear constraints and $$T$$ quadratic terms in the objective function. As a result, the computation time required to solve this problem would increase very mildly as a function of $$n$$. This implies that a very large scale MV model can now be solved in a practical amount of time.

##### MSC:
 91B28 Finance etc. (MSC2000) 90C90 Applications of mathematical programming 90C06 Large-scale problems in mathematical programming 90C20 Quadratic programming 90-08 Computational methods for problems pertaining to operations research and mathematical programming
##### Keywords:
portfolio optimization
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