Efficient way of picking a subset that fulfills criteria

In summary, the conversation discusses a set of elements, S, and its subset, P, which must satisfy certain conditions based on functions f and g. The goal is to efficiently find which elements meet the conditions. One approach is to use brute force, but there may be a more efficient method if f and g are monotonic. This involves finding specific values for d1, d2, and d3 and rewriting the selection criteria in terms of these values. A binary search can then be used to find the remaining boundary values.
  • #1
scienalc
16
0
Let's say there is a set of n elements, S. P is a subset of S, with m elements, and satisfies some conditions, i.e. c1 < f(P) < c2 and g(P) < c3, where f and g are some functions on the elements of P and c1, c2 and c3 are constants.

For practical purposes, S is represented as a sorted array of n floating point numbers.

The objective is to find which elements satisfy the above conditions.

My current approach is rather brute force, i.e. exhaustively trying out combinations. I'm wondering whether there is some more efficient way.
 
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  • #2
In the general case of any f and g, no.

But, for example, if f and g are monotonic, then yes.
In that case:
-- find d1, d2, and d3 for f(d1)=c1; f(d2)=c2; and g(d3)=c3.
-- rewrite the selection criteria in terms of d1, d2, and d3: d1<P<d2 or d1>P>d2 and d3>P or d3<P.
-- if possible, combine those ranges. For example if d1<P<d2; P>d3; and d1<d3; then only d3<P<d2 is needed.
-- do a binary search in S for the d1, d2, and d3 values that remain boundary values.
 

What is the purpose of picking a subset that fulfills criteria?

Picking a subset that fulfills criteria allows for a more efficient and targeted analysis of data, as it focuses only on the relevant subset rather than the entire dataset. This can save time and resources in research and experimentation.

How do you determine the most efficient way of picking a subset that fulfills criteria?

The most efficient way of picking a subset that fulfills criteria depends on the specific criteria and data being analyzed. Generally, it involves using algorithms and statistical methods to identify and select the relevant subset.

What are some common criteria used for picking a subset?

Some common criteria used for picking a subset include specific data values or ranges, statistical significance, and relevance to the research question or hypothesis. The criteria chosen will depend on the specific goals of the analysis.

Can picking a subset that fulfills criteria improve the accuracy of results?

Yes, picking a subset that fulfills criteria can improve the accuracy of results by eliminating irrelevant data and focusing on the most relevant subset. This can reduce bias and increase the precision of the analysis.

Are there any potential challenges or limitations to picking a subset that fulfills criteria?

One potential challenge is determining the most appropriate criteria to use for picking a subset. Additionally, there may be limitations in the data itself or in the algorithms used to select the subset. It is important to carefully consider and validate the criteria chosen to ensure the subset accurately represents the larger dataset.

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