Efficient way of picking a subset that fulfills criteria

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SUMMARY

The discussion focuses on optimizing the selection of a subset P from a sorted array S of n floating point numbers, where P must satisfy specific conditions defined by functions f and g. The brute force method of exhaustively trying combinations is inefficient. When f and g are monotonic, a more efficient approach involves determining boundary values d1, d2, and d3, and rewriting selection criteria accordingly. This allows for the application of binary search to quickly identify valid elements in S that meet the criteria.

PREREQUISITES
  • Understanding of monotonic functions
  • Familiarity with binary search algorithms
  • Knowledge of sorted arrays
  • Basic concepts of subset selection and criteria evaluation
NEXT STEPS
  • Study the implementation of binary search in sorted arrays
  • Explore monotonic function properties and their implications in optimization
  • Research techniques for rewriting selection criteria in mathematical optimization
  • Investigate advanced subset selection algorithms for performance improvement
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Data scientists, algorithm developers, and software engineers focused on optimizing data selection processes and improving computational efficiency in array manipulation.

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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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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.
 
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