# matching

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Related to Matching problem: Maximum weight matching

## Cash Flow Matching

The practice of matching returns on a portfolio to future capital outlays. That is, cash flow matching involves investing in certain securities with a certain expected return so that the investor will be able to pay for future liabilities. Pension funds and annuities perform the most cash flow matching, as they have future liabilities that are both large and relatively easy to estimate. Portfolios that perform cash flow matching usually invest in low-risk, investment-grade securities. The practice is also called portfolio dedication, matching, or the structured portfolio strategy.

## matching

the process of matching revenues and profits with the associated expenses incurred in earning these revenues. See ACCRUALS PRINCIPLE OF ACCOUNTING.
References in periodicals archive ?
They prove that the permutation pattern matching problem is NP-complete when [sigma] has no decreasing subsequence of length 3 and [pi] has no decreasing subsequence of length 4.
Wang, "A new algorithm and application of solving maximum matching problem of bipartite graph," Computer Systems & Applications, vol.
Roughly speaking, modulo transfer equivalence there always exists a feedback compensator which solves the model matching problem for given F(z) and G(z).
Steps 2-6 are the distributed algorithm for maximum weighted matching problem.
24] Abdulwahab Ali Almazroi, A Fast Hybrid Algorithm Approach for the Exact String Matching Problem Via Berry Ravindran and Alpha Skip Search Algorithms.
The matching problem is often presented in more concrete guises.
The classical pattern matching problem is to find all the occurrences of a given pattern P = P[1.
This can be achieved by solving a weighted bipartite matching problem, with the weight of each edge equal to the degree of the column vertex it is incident on.
In this section, we define an appropriate discrete deformable template along orthogonal curves according to the 2-D polygonal objects template matching problem.
The compressed matching problem was first defined in the work of Amir and Benson [1992] as the task of performing string matching in a compressed text without decompressing it.
Thus, there is no matching problem and the data requirements are greatly reduced.
This again involves a matching problem, now between features from two images, rather than an image and a model.

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