Optimizing Sorting Methods for Efficiency

Question:

Hello everyone, I am currently seeking a more efficient method for sorting my data. In my dataset, which consists of 233 positive integers ranging from 0 to 20160, I am trying to identify the smallest non-zero value, which I will refer to as Ntarget. One way I have managed to accomplish this is by using rungs 100 and 101 to generate integers and insert them into the array A_Start_Compare[...]. These integers range from -20160 to +20160, but I am specifically interested in the positive integers. Rung 102 helps eliminate the negative integers using a GRT. Ideally, I would like to simply subtract the integer from B_Next_Water[0] and then use SRT. However, the issue arises when the generated numbers after GRT fall between 0 and +20160. My goal is to pinpoint the smallest non-zero number (Ntarget), which can be located anywhere in the array. To achieve this, I realized I need to position the desired number at either the top (B_Next_Water[232]) or bottom (B_Next_Water[0]) of the SRT so that I can easily reference its exact array position. To accomplish this, I initially manipulate Ntarget by making it the largest number through subtraction from +20160. This enables Ntarget to consistently reside at B_Next_Water[232] after SRT, thereby streamlining the process. While this method is effective, I am open to exploring alternative approaches for improved efficiency. Any suggestions on how to enhance this process are greatly appreciated.

Top Replies

Are you looking to sort the array, or are you specifically seeking the smallest value greater than zero? It's important to clarify your goal in order to determine the best approach.

Inquiring about the need to sort the array or just finding the lowest value above 0? I am solely focused on identifying the lowest positive value. Is there a method to achieve this?

TheHoneyBadger is seeking advice on optimizing a code snippet. One suggestion is to reverse the Source A/B parameters in the first SUB element on rung 102, eliminating the need for a second SUB element on the same rung. This adjustment will result in the SUB element on rung 103 becoming SUB 0 B_Next_Water[231(232?)]. In the code snippet, when the one-shot detects the rising edge of Test[0].0 on rung 100, it populates a 233-element array with values of 0 and sets B_Next_Water_Calc to 1 to initiate data sampling. If bitOSF_Flashing_500ms is 1 on the same scan cycle, the CTU on the next rung will be triggered to increment Trig_Next_Water.ACC from 0 to 1, leaving a 0 in B_Next_Water[0]. However, if bitOSF_Flashing_500ms is 0 when B_Next_Water_Calc is set to 1, a non-negative value may end up in B_Next_Water[0], potentially impacting downstream processes. This discrepancy could lead to the array being filled with a mix of 0s and 1s. Additionally, the inversion of values on rung 102, along with the GRT instruction at the rung's beginning, may be causing confusion. Overall, reevaluating the logic and parameter configurations in the code can help streamline its functionality.

The honey badger mentioned that Rung 102 effectively removes negative integers with the help of a GRT. In addition, it gets rid of smaller positive numbers when B_Next_Water[0] is non-zero. Click here to learn more about this process!

In the comment thread, Dr. Bitboy suggested reversing the Source A/B parameters in the first SUB on rung 102 to streamline the code. By making this adjustment, the need for the second SUB on rung 102 is eliminated, and the SUB on rung 103 is modified to become SUB 0 B_Next_Water[231(232?)].TL;DR: The current code logic may not be functioning correctly. On rung 100, the one-shot trigger detects the rising edge of Test[0].0, populates a 233-element array with values of 0, and sets a value of 1 into B_Next_Water_Calc to initiate data sampling. If bitOSF_Flashing_500ms is 1 on the same scan cycle, the input rung to the CTU on the following rung is activated, incrementing Trig_Next_Water.ACC from 0 to 1. This can lead to a scenario where a non-negative value affects downstream operations if bitOSF_Flashing_500ms is 0 when the value 1 is latched into B_Next_Water_Calc. Adjustments and clarifications are needed to ensure proper functionality and data handling. Feel free to try alternative methods like SRT or FAL to optimize the code further.

Have you considered using an approach where you parse through the data once and keep track of the smallest positive number? By comparing each element in your array to a variable that you've initially set to the maximum value possible, you can efficiently update this variable whenever you come across a smaller non-zero value. This way, you'd avoid unnecessary sorting. In terms of complexity, it is efficient as it's a linear time algorithm (O(n)), and sorting typically requires O(n log n) time. It would also significantly reduce the code complexity of your current setup. Hope this helps!

It sounds like you've put quite a bit of thought into your current method, but I'd like to suggest looking into Heap Sort. This model can establish a more efficient sorting system, effectively managing both positive and negative integers. It's designed to maintain the highest or lowest number (depending on if it's a max heap or min heap) as the root of the structure at all times, which could inherently separate your negative values from your positives. This method maintains a time complexity of O(n log n), and it would easily place your target number at the top of the heap for immediate reference, reducing your need for additional manipulation.

It sounds like you've found a complex, but effective, way to achieve your end goal. Kudos to your critical thinking! But indeed, it seems a little overwrought. An alternative could be using a more direct approach by writing a simple function to iterate over your dataset and directly find the smallest non-zero positive integer. This function would compare the current integer to a saved "smallest integer" (which you could initially set to a value larger than any in your dataset). If the current integer is smaller and non-zero, it replaces the saved one. This would significantly reduce your processing time as it would only need to traverse your dataset once, and wouldn't require sorting or generating new numbers. However, this suggestion is more along the lines of general programming logic - you might have to adopt it to your specific scenario based on what automation platform you're working with.

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Frequently Asked Questions (FAQ)

FAQ: 1. Question: What is the goal of the sorting process described in the discussion thread?

Answer: Answer: The goal is to identify the smallest non-zero value, referred to as Ntarget, from a dataset of 233 positive integers ranging from 0 to 20160.

FAQ: 2. Question: How is the sorting process currently being conducted in the dataset?

Answer: Answer: The process involves generating integers in rungs 100 and 101 and inserting them into the array A_Start_Compare[], eliminating negative integers in rung 102 using a GRT, and manipulating Ntarget to position it at either the top (B_Next_Water[232]) or bottom (B_Next_Water[0]) of the SRT.

FAQ: 3. Question: What issue arises when the generated numbers fall within the range of 0 to +20160 after the GRT filter?

Answer: Answer: The issue arises when the generated numbers fall within this range, making it challenging to pinpoint the smallest non-zero number (Ntarget) in the array.

FAQ: 4. Question: How is the desired number positioned at either the top or bottom of the SRT to simplify referencing its array position?

Answer: Answer: The desired number (Ntarget) is manipulated to become the largest number through subtraction from +20160, ensuring it consistently resides at B_Next_Water[232] after the SRT operation.

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