NP Not Equal to P: 2nd Considerations

NP not = to P:  Further Considerations & Proofs, Evidences.

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By Herb Wiggins, M.D.; Clinical Neurosciences; Discoverer/Creator of the Comparison Process/CP Theory/Model; 14 Mar. 2014.
Copyright © July 23, 2019
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For P ~= to NP, there are three big, logical, empirical proofs that it’s the case.
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First of all, the NP is Sudoku and X-words, & picture puzzle solutions. In order to make them solvable we MUST add information. The hardest Suduko has the fewest numbers, usually 26 or less of the 81 total possible.. Adding numbers, that is info, makes it more easily solvable.These are the cases of the problems being known with specific solutions. Not ALL NP is like that, however.
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Now if we add more columns in Sudoku, 10 by 10, and upwards of 100 by 100, it cannot be solved at all, even with our best computers, until those get faster. At which time it will likely solve those, too. That increases the sorting difficulty, which always makes NP harder.
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The same is true of X-words. The most specific clues make it easiest. The more general clue, which create a much larger sorting problem, makes it hard. IOW, if the clue states that the word is a name of a state capital, the sorting problem becomes very hard. Esp. if the state capital is NOT in the US, but included all other nations with states. But if it’s IDAHO’s capital, it’s Boise, easily. The sorting problem is easier. If the clue is for a foreign word, then it’s even harder, too. If the word is not stated to be one language or another, then it’s impossible to solve.
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And THAT also shows us why NP can be hard to solve. It’s a matter of the neuroscience of problem solving. The harder the sorting problems, the longer it takes. Using a computer can sort out things faster, in many cases. But creatively solving problems most computers cannot do. They cannot create music a la Tchaikovsky or Mancini, but a good composer can create “In Stylo” of. Nor can AI duplicate the findings of Einstein.
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The same is true of the visual sorting out of solutions to picture puzzles. We use in those a series of strategies. To start, we find the straight edges and put those together. This creates a structure which simplifies our sorting. As we continue to add more pieces, the solutions get faster and faster, kthus showing that the pictures with the least numbers of pieces are the easiest to solve, because we are not checking against all the others.
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Next we find the patterns of the colours, shapes, and person’s faces, etc. to combine around the edges. And then emplace those it. But note, if we don’t have an image of the puzzle solved, it’s very hard. So if we do, Information is Added yet again and that facilitates the solution. In every case, adding information by reducing the numbers of puzzle pieces by steadily solving it, increases the solution speeds, as well. Just as in Sudoku and X-word puzzles. This is a generally known fact. But these are for puzzles which we KNOW can be solved.
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What of the other category of hard NP, where the solutions are NOT known, and which must be sorted out and solved by other methods? That’s a problem, very much so, too.
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However, understanding professional skill sets being more efficient than amateurs WE can solve problems more easily simply by doing the work of finding out the least energy skill sets of the professionals, & then improving those also, without limits. Or how to solve the NP problem of creating a self-driving care by expert systems methods.
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Or why Uber Grew and did so well: A Growth S-curve of more efficient, least energy methods.
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In more specific cases how did Einstein solve the problems of physics? First of all, he never left many clues or references which specifically stated 1 way or the other. So we must guess. & that’s not helpful here.

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However, Einstein did states some general rules. If we simplify the problem, then the solutions are much, much faster. And indeed, he writes that simplifications ARE the hallmarks of solutions to major problems. He also stated that almost every advance in physics is preceded by an epistemological advance, which is just another way of saying the same thing. IOW, it’s part of the puzzle solving tools in our professional tool box between our ears.
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How did Edison create the electric light? That’s easier. First, he saw that when a current was run through copper at high currents it glowed. And then he realized that electricity would create light. So he had to solve a series of problems, heating and melting of the filament, and he did that by Heavy Duty sorting, of Trial and Error. It’s goal oriented. Match the outcomes of the tested substances to the goals, AGAIN, CP.  Did the material work or not? Have written before of Tesla’s likely method of Refractoriness of materials, i.e., adding a big concept capable of reaching the solution in a few weeks, compared to Edison’s “brute force” approach. Which please note, ALSO simplified down the solution from 2 years to only a few weeks. & that cut the Gordian knot of complexity, very likely. It vastly simplified the sorting task down to Mo, Rh, Tungsten, carbon and a few others.
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Scroll down about 1/2 way to the discussion in detail of the creation of the electric light by Edison and HOW, very simply Tesla could have solved it in a few weeks. Or how they found tungstne eyars later as a successor to carbon filmants of Edison.
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And we see the huge sorting problems ongoing yet again in the practical solution fields. & those are analogous to creativity problems in Info Theory (IT). We must FIRST figure out how the brain solves problems in order to solve the NP ~ = to P. And this has been largely done. We must add information OUTSIDE of the problem conditions, in order to solve it in an Einsteinian way. Which is essentially what he stated. Problem solutions usually lie OUTSIDE of the mindset which finds the problem. IOW, we must go outside of the box, see the forest for the trees, see the wider problem, etc. And THAT here, is also the case. We MUST see the wider picture of how problems are solved in IT. And that has been done here to some extent, but not completely.
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Thus NP is hard because it’s a very hard sorting problem, not easily amenable to polynomial solutions. Adding information makes that more solvable, because, like in Sudoku and X-words those are made Easier by adding numbers and letters & words. It cannot be solved easily by logic, words, or math. But in visualizing what’s going on. Much like how we walk about using Least energy, optimized routes for distances and times.
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This is the general problem of problem solving, in short. Using IT, a statement with more information has less entropy and is a higher energy state. It has more information because it circumscribes the possibilities more, too. Therefore, the NP ~= to P is easily solved. If NP is not solved, it’s hard to solve. When solved, it has more information. We can see this by comparing most all NP problems in the past, with their solutions today. Information has been added to solve it. And adding information means that NP is NOT = to P.
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Some problems are intrinsically insolvable, with this proviso: by using Current methods!!!
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The solution to those is likely the Qu Computer, when that can be sorted out to a good solution.
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However, and this is the case, most all time consuming sortings CAN be sped up, as in the Edison light bulb case above. IOW, by, as so many have stated including Einstein, H. D, Thoreau and others, such as Feynman creating his diagrams, “cutting the Gordian knot of complexity” by simplification.
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Here’s is yet another method to solve the prime number sorting problem of the RSA. If we take a fast and vast simplification of the number and repeating prime patterns down to a quartets method, which repeats every 30 numbers in the decimal system, we get rid of all but 24 numbers per 90. Then by a simple repeating algorithm we sort out all of those not prime, Prime multiples (PM) numbers ending in the quartets of -1, -3, -7, -9. That can be done at ANY point in the number line, high or low. by sorting out the KNOWN primes, & that remaining number line can be reduced by 1000’s, leaving mostly primes. By noting that most, the majority of primes are most often in the Rem1 quartet, it can be sped up even more. There will be other ways found of doing that, too. In addition the Rem0 quartet of the trio is more likely to have primes than the Rem2 quartet. & other such findings.
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And then by using that method to rid the number line of PM numbers in the 100’s of digits, we can use another system (compound method) which is efficient to find the primes, very quickly.
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This is how it’s done. Therefore, given the likely unlimited number of ways of simplifying down the problems of sorting, the Polynomial limit isn’t likely the case, either. Thus NP is Not = to P, because any new, effective creative system is Also improving without limits.
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 Some NP are more solvable, such as how far is it to Houston from St Louis, but that depends on HOW we go there, and all of that complexity involved. There is NO exact solution to that distance, either, unless we specify more completely the route and means we are taking. And even then the math does not apply except as an approximation. There is NO absolute, nor exact, nor perfect solution to that practical problem. But we can nearly universally add info to make it solved.
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We need only look at the developments of the autos and our flying methods over the last 200-100 years to see that. Improvement is possible without limits, very likely.
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In making diagnoses of medical conditions we solve these problems of NP all the time. We must FIRST make a medical diagnosis in the region of the organ affected. That narrows the field, and it adds information. Then we test/CP by history and physical and get as much information to solve the problem as possible. If it’s still not clear, we get more information by doing the tests which we know are likely to help us the most, that is efficiency. In every instance we are adding information using efficient methods to acquire that and then use it to make the diagnoses.
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We create the treatment protocols and medications and methods by the same ways. In each and every field of medicine.
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This is part and parcel of the problem solving which NP Not = to P is part of. So if we want solutions to a problem, in a practical sense we MUST show, how, creatively we take NP to P. And in every case, that’s adding medical information until the solution, if possible is reached. Not all problems can be solved. Not all conditions CAN be diagnosed. Even in the bvst diagnostic centers, for instance, we cannot figure out about 50% of peripheral neuropathy Cause, or more likely causes. It’s NOT a true or false, yes or no, it’s “this or not this”. It’s NOT logical, mathematical or verbal in the first forms. No Empirical False Dichotomies allowed!! Solutions can be partial. In addition better solutions over time are very likely possible in most every area, too. Sometimes it takes time to diagnose a problem. We do that by successive exams and testings. Occ. it takes a test a long time to come back.  And this is the case.
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Sometimes the diagnoses are NOT fully clear until the Parkinson’s like syndrome matures and the double vision starts, as in Progressive Supranuclear Palsy, which often in early stages, acts like Parkinson’s but rarely responds to L-dopa therapies. But when, and this has happened to many, the eye movements start to vary from normal, by CP testing, then the DX is made. We ADD information to solve NP to P. And there that is yet again, the solution to NP not = to P.
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NP has less information than P.  NP is not complete, either. And in order to solve it, we add medical information. It’s NOT is NP = to P or not. It’s a probabilities of NP being = to P. Thus logic fails in these cases. It’s not All or none, white or black, A or Not A. Those are false dichotomies exposed by empiricism. It’s the multiplicity of complex systems at work here. Shannon’s rules of information content of NP compared to P show this consistently. The better the description of an event, the more information content it has. Thus NP is not = to NP.
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That’s yet another proof. and the last is that “There is no absolute information.” The epistemological of Einstein, altogether true, states that it’s the case because of the nature of measuring, no absolute space or time. And when that is converted to describing events, by CP methods, we see that the same injunction against absolute descriptions is the case. There is NO final, complete description likely or possible. The point being that because there is NO perfect heat engine, there is also NO Info Theory perfect description, either. Our information is highly likely to be most all incomplete. And thus is there is unlikely to be absolute information.
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That’s the last proof of NP ~ = to P. As long as they are not both tautologies, that is.
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Simple, elegant easy to prove at least 4 ways!!!
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& info almost always decays in time as well, thus making it more incomplete than ever!!!! We cannot solve some problems of past history because the info is gone. That is a huge problem in genealogy, law, forensics, paleontology AND history. But not quite.
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That’s why the Tanis site in South Dakota was so incredibly useful to us regarding the KT boundary event. Robert DePalma worked on It,  ADDED information and solved the big 3 meter geological layers problem, where there were before no Dinosaurs found. They found Dino remains!!!! It showed largely what happened ON That Day!!! But only to probabilities. It mixed the terrestrial and marine layers in a lovely way, too, showing the asteroid impact effects both on land and coastal marine systems.
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Thus NP is not = to P.

 

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