狠狠撸shows by User: SingKuangTan

狠狠撸shows by User: SingKuangTan / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: SingKuangTan / Wed, 22 Feb 2023 09:33:01 GMT 狠狠撸Share feed for 狠狠撸shows by User: SingKuangTan neuron_never_overfits.pptx /slideshow/neuronneveroverfitspptx/256012744 neuronneveroverfits-230222093301-3afbc80f
Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning.]]>
Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning.]]> Wed, 22 Feb 2023 09:33:01 GMT /slideshow/neuronneveroverfitspptx/256012744 SingKuangTan@slideshare.net(SingKuangTan) neuron_never_overfits.pptx SingKuangTan Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/neuronneveroverfits-230222093301-3afbc80f-thumbnail.jpg?width=120&height=120&fit=bounds" /> Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning.

neuron_never_overfits.pptx from Sing Kuang Tan

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0 Square Peg Problem Test Function /SingKuangTan/sqrpegprobtestfnpptx sqrpegprobtestfn-220529081831-ea382bc6
Describe a set of linear equations to formulate a test function for Square Peg Problem. kungfu computer science, geometric complexity theory]]>
Describe a set of linear equations to formulate a test function for Square Peg Problem. kungfu computer science, geometric complexity theory]]> Sun, 29 May 2022 08:18:31 GMT /SingKuangTan/sqrpegprobtestfnpptx SingKuangTan@slideshare.net(SingKuangTan) Square Peg Problem Test Function SingKuangTan Describe a set of linear equations to formulate a test function for Square Peg Problem. kungfu computer science, geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sqrpegprobtestfn-220529081831-ea382bc6-thumbnail.jpg?width=120&height=120&fit=bounds" /> Describe a set of linear equations to formulate a test function for Square Peg Problem. kungfu computer science, geometric complexity theory

Square Peg Problem Test Function from Sing Kuang Tan

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0 Convex Square Peg Problem /slideshow/conv-sqr-pegprob/251353026 convsqrpegprob-220315121559
A visual proof for Toeplitz Square Peg Problem of convex shapes, kungfu computer science, geometric complexity theory]]>
A visual proof for Toeplitz Square Peg Problem of convex shapes, kungfu computer science, geometric complexity theory]]> Tue, 15 Mar 2022 12:15:58 GMT /slideshow/conv-sqr-pegprob/251353026 SingKuangTan@slideshare.net(SingKuangTan) Convex Square Peg Problem SingKuangTan A visual proof for Toeplitz Square Peg Problem of convex shapes, kungfu computer science, geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/convsqrpegprob-220315121559-thumbnail.jpg?width=120&height=120&fit=bounds" /> A visual proof for Toeplitz Square Peg Problem of convex shapes, kungfu computer science, geometric complexity theory

Convex Square Peg Problem from Sing Kuang Tan

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0 Visual topology /slideshow/visual-topology/251156570 visualtopology-220211135625
Use 2d visual proof to prove well known Topology problems. kung fu computer science, geometric complexity theory]]>
Use 2d visual proof to prove well known Topology problems. kung fu computer science, geometric complexity theory]]> Fri, 11 Feb 2022 13:56:24 GMT /slideshow/visual-topology/251156570 SingKuangTan@slideshare.net(SingKuangTan) Visual topology SingKuangTan Use 2d visual proof to prove well known Topology problems. kung fu computer science, geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/visualtopology-220211135625-thumbnail.jpg?width=120&height=120&fit=bounds" /> Use 2d visual proof to prove well known Topology problems. kung fu computer science, geometric complexity theory

Visual topology from Sing Kuang Tan

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0 Implement Data Structures with Python Fast using Sparse Matrix /slideshow/data-structures-251058000/251058000 datastructures-220126090159
Implement Data Structures with Python Fast using Sparse Matrix. kung fu computer science, geometric complexity theory]]>
Implement Data Structures with Python Fast using Sparse Matrix. kung fu computer science, geometric complexity theory]]> Wed, 26 Jan 2022 09:01:58 GMT /slideshow/data-structures-251058000/251058000 SingKuangTan@slideshare.net(SingKuangTan) Implement Data Structures with Python Fast using Sparse Matrix SingKuangTan Implement Data Structures with Python Fast using Sparse Matrix. kung fu computer science, geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datastructures-220126090159-thumbnail.jpg?width=120&height=120&fit=bounds" /> Implement Data Structures with Python Fast using Sparse Matrix. kung fu computer science, geometric complexity theory

Implement Data Structures with Python Fast using Sparse Matrix from Sing Kuang Tan

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0 Discrete Markov Random Field Relaxation /slideshow/discrete-markov-random-field-relaxation/250899528 approxmrf-211226050110
This powerpoint gives a technique to approximate (relaxation) discrete Markov Random Field (MRF) using convex programming. This approximated MRF can be used to approximate NP problem. This also proves that NP is not equal P because the MRF convex programming and the approximate MRF convex programming are not the same with removal of some product terms. kung fu Computer Science, Geometric complexity theory]]>
This powerpoint gives a technique to approximate (relaxation) discrete Markov Random Field (MRF) using convex programming. This approximated MRF can be used to approximate NP problem. This also proves that NP is not equal P because the MRF convex programming and the approximate MRF convex programming are not the same with removal of some product terms. kung fu Computer Science, Geometric complexity theory]]> Sun, 26 Dec 2021 05:01:09 GMT /slideshow/discrete-markov-random-field-relaxation/250899528 SingKuangTan@slideshare.net(SingKuangTan) Discrete Markov Random Field Relaxation SingKuangTan This powerpoint gives a technique to approximate (relaxation) discrete Markov Random Field (MRF) using convex programming. This approximated MRF can be used to approximate NP problem. This also proves that NP is not equal P because the MRF convex programming and the approximate MRF convex programming are not the same with removal of some product terms. kung fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/approxmrf-211226050110-thumbnail.jpg?width=120&height=120&fit=bounds" /> This powerpoint gives a technique to approximate (relaxation) discrete Markov Random Field (MRF) using convex programming. This approximated MRF can be used to approximate NP problem. This also proves that NP is not equal P because the MRF convex programming and the approximate MRF convex programming are not the same with removal of some product terms. kung fu Computer Science, Geometric complexity theory

Discrete Markov Random Field Relaxation from Sing Kuang Tan

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0 NP vs P Proof using Deterministic Finite Automata /SingKuangTan/np-vs-pdfa npvspdfa-211119151618
Prove that Clique problem is NP and cannot be reduced to P because the Deterministic Finite Automata of the Clique problem has exponential number of states. We can use the same concept to prove that NP is not equal to P using Turing Machine. We figured out a way to unify Mathematics. This proof is for those Theoretical Computing guys who do not know Boolean algebra but know Turing Machine. Kung fu computer science, Geometric complexity theory]]>
Prove that Clique problem is NP and cannot be reduced to P because the Deterministic Finite Automata of the Clique problem has exponential number of states. We can use the same concept to prove that NP is not equal to P using Turing Machine. We figured out a way to unify Mathematics. This proof is for those Theoretical Computing guys who do not know Boolean algebra but know Turing Machine. Kung fu computer science, Geometric complexity theory]]> Fri, 19 Nov 2021 15:16:18 GMT /SingKuangTan/np-vs-pdfa SingKuangTan@slideshare.net(SingKuangTan) NP vs P Proof using Deterministic Finite Automata SingKuangTan Prove that Clique problem is NP and cannot be reduced to P because the Deterministic Finite Automata of the Clique problem has exponential number of states. We can use the same concept to prove that NP is not equal to P using Turing Machine. We figured out a way to unify Mathematics. This proof is for those Theoretical Computing guys who do not know Boolean algebra but know Turing Machine. Kung fu computer science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/npvspdfa-211119151618-thumbnail.jpg?width=120&height=120&fit=bounds" /> Prove that Clique problem is NP and cannot be reduced to P because the Deterministic Finite Automata of the Clique problem has exponential number of states. We can use the same concept to prove that NP is not equal to P using Turing Machine. We figured out a way to unify Mathematics. This proof is for those Theoretical Computing guys who do not know Boolean algebra but know Turing Machine. Kung fu computer science, Geometric complexity theory

NP vs P Proof using Deterministic Finite Automata from Sing Kuang Tan

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0 Use Inductive or Deductive Logic to solve NP vs P? /slideshow/inductive-deductive-npvsp/250563145 inductivedeductivenpvsp-211031040020
Use Inductive or Deductive Logic to solve NP vs P? I use circuit complexity and deductive logic to solve NP vs P. Kung fu computer science, Geometric complexity theory]]>
Use Inductive or Deductive Logic to solve NP vs P? I use circuit complexity and deductive logic to solve NP vs P. Kung fu computer science, Geometric complexity theory]]> Sun, 31 Oct 2021 04:00:20 GMT /slideshow/inductive-deductive-npvsp/250563145 SingKuangTan@slideshare.net(SingKuangTan) Use Inductive or Deductive Logic to solve NP vs P? SingKuangTan Use Inductive or Deductive Logic to solve NP vs P? I use circuit complexity and deductive logic to solve NP vs P. Kung fu computer science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/inductivedeductivenpvsp-211031040020-thumbnail.jpg?width=120&height=120&fit=bounds" /> Use Inductive or Deductive Logic to solve NP vs P? I use circuit complexity and deductive logic to solve NP vs P. Kung fu computer science, Geometric complexity theory

Use Inductive or Deductive Logic to solve NP vs P? from Sing Kuang Tan

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0 Clique problem step_by_step /slideshow/clique-problem-stepbystep/250288998 cliqueproblemstepbystep-210924150440
Simplify a Clique Problem Boolean algebra by factorization. Show that Clique Problem is Non-Deterministic Polynomial Time (NP) and cannot be simplified to Polynomial Time (P). Kung Fu Computer Science, Geometric complexity theory]]>
Simplify a Clique Problem Boolean algebra by factorization. Show that Clique Problem is Non-Deterministic Polynomial Time (NP) and cannot be simplified to Polynomial Time (P). Kung Fu Computer Science, Geometric complexity theory]]> Fri, 24 Sep 2021 15:04:40 GMT /slideshow/clique-problem-stepbystep/250288998 SingKuangTan@slideshare.net(SingKuangTan) Clique problem step_by_step SingKuangTan Simplify a Clique Problem Boolean algebra by factorization. Show that Clique Problem is Non-Deterministic Polynomial Time (NP) and cannot be simplified to Polynomial Time (P). Kung Fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cliqueproblemstepbystep-210924150440-thumbnail.jpg?width=120&height=120&fit=bounds" /> Simplify a Clique Problem Boolean algebra by factorization. Show that Clique Problem is Non-Deterministic Polynomial Time (NP) and cannot be simplified to Polynomial Time (P). Kung Fu Computer Science, Geometric complexity theory

Clique problem step_by_step from Sing Kuang Tan

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0 Beyond Shannon, Sipser and Razborov; Solve Clique Problem like an Electronic Engineer /slideshow/beyond-shannon-sipser-and-razborov-solve-clique-problem-like-an-electronic-engineer/250088045 cliqueproblemmonotonecircuit-210831144534
Convert any Boolean algebra into monotone circuit and use that to prove that NP is not equal to P as monotone circuit cannot solve Clique problem in Polynomial time complexity. NP vs P is a Millennium Prize problem. Kung Fu Computer Science, Geometric complexity theory]]>
Convert any Boolean algebra into monotone circuit and use that to prove that NP is not equal to P as monotone circuit cannot solve Clique problem in Polynomial time complexity. NP vs P is a Millennium Prize problem. Kung Fu Computer Science, Geometric complexity theory]]> Tue, 31 Aug 2021 14:45:33 GMT /slideshow/beyond-shannon-sipser-and-razborov-solve-clique-problem-like-an-electronic-engineer/250088045 SingKuangTan@slideshare.net(SingKuangTan) Beyond Shannon, Sipser and Razborov; Solve Clique Problem like an Electronic Engineer SingKuangTan Convert any Boolean algebra into monotone circuit and use that to prove that NP is not equal to P as monotone circuit cannot solve Clique problem in Polynomial time complexity. NP vs P is a Millennium Prize problem. Kung Fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cliqueproblemmonotonecircuit-210831144534-thumbnail.jpg?width=120&height=120&fit=bounds" /> Convert any Boolean algebra into monotone circuit and use that to prove that NP is not equal to P as monotone circuit cannot solve Clique problem in Polynomial time complexity. NP vs P is a Millennium Prize problem. Kung Fu Computer Science, Geometric complexity theory

Beyond Shannon, Sipser and Razborov; Solve Clique Problem like an Electronic Engineer from Sing Kuang Tan

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0 A Weird Soviet Method to Partially Solve the Perebor Problem /slideshow/a-weird-soviet-method-to-partially-solve-the-perebor-problem-250017730/250017730 weirdsovietperebor-210820105936
Monotone Circuit can implement an algorithm to run Non-Deterministic Polynomial time complexity (NP) problem in Polynomial time complexity (P). I developed a method to implement all algorithms without "Not" operations. Using this information, I manage to prove that NP is not equal to P. Kung Fu Computer Science, Geometric complexity theory]]>
Monotone Circuit can implement an algorithm to run Non-Deterministic Polynomial time complexity (NP) problem in Polynomial time complexity (P). I developed a method to implement all algorithms without "Not" operations. Using this information, I manage to prove that NP is not equal to P. Kung Fu Computer Science, Geometric complexity theory]]> Fri, 20 Aug 2021 10:59:36 GMT /slideshow/a-weird-soviet-method-to-partially-solve-the-perebor-problem-250017730/250017730 SingKuangTan@slideshare.net(SingKuangTan) A Weird Soviet Method to Partially Solve the Perebor Problem SingKuangTan Monotone Circuit can implement an algorithm to run Non-Deterministic Polynomial time complexity (NP) problem in Polynomial time complexity (P). I developed a method to implement all algorithms without "Not" operations. Using this information, I manage to prove that NP is not equal to P. Kung Fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/weirdsovietperebor-210820105936-thumbnail.jpg?width=120&height=120&fit=bounds" /> Monotone Circuit can implement an algorithm to run Non-Deterministic Polynomial time complexity (NP) problem in Polynomial time complexity (P). I developed a method to implement all algorithms without "Not" operations. Using this information, I manage to prove that NP is not equal to P. Kung Fu Computer Science, Geometric complexity theory

A Weird Soviet Method to Partially Solve the Perebor Problem from Sing Kuang Tan

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0 NP vs P 的简要说明。使用马尔可夫随机场和布尔代数简化证明 Np 不等于 P /slideshow/brief-np-vspexplainchinese/249934098 briefnpvspexplainchinese-210806165415
在本文中，我们证明了非确定性多项式时间复杂度（NP）不等于多项式时间复杂度（P）。我们开发了布尔代数，它将推断非确定性多项式计算时间马尔可夫随机场的两个变量的解。我们证明了无论我们如何简化布尔代数，它都不能在多项式计算时间内运行（NP 不等于 P）。我们还开发了证明所有多项式计算时间多层布尔代数都可以转换为另一个多项式计算时间多层布尔代数，其中第一层只有“非”运算。所以在简化布尔代数的过程中，我们只需要考虑在第一层只假设'非'操作的因式分解操作。我们还为马尔可夫随机场链和以马尔可夫随机场形式表示的 2sat 问题开发了多项式计算时间布尔代数，以给出多项式计算时间马尔可夫随机场的示例。功夫计算机科学, 几何复杂性理论]]>
在本文中，我们证明了非确定性多项式时间复杂度（NP）不等于多项式时间复杂度（P）。我们开发了布尔代数，它将推断非确定性多项式计算时间马尔可夫随机场的两个变量的解。我们证明了无论我们如何简化布尔代数，它都不能在多项式计算时间内运行（NP 不等于 P）。我们还开发了证明所有多项式计算时间多层布尔代数都可以转换为另一个多项式计算时间多层布尔代数，其中第一层只有“非”运算。所以在简化布尔代数的过程中，我们只需要考虑在第一层只假设'非'操作的因式分解操作。我们还为马尔可夫随机场链和以马尔可夫随机场形式表示的 2sat 问题开发了多项式计算时间布尔代数，以给出多项式计算时间马尔可夫随机场的示例。功夫计算机科学, 几何复杂性理论]]> Fri, 06 Aug 2021 16:54:15 GMT /slideshow/brief-np-vspexplainchinese/249934098 SingKuangTan@slideshare.net(SingKuangTan) NP vs P 的简要说明。使用马尔可夫随机场和布尔代数简化证明 Np 不等于 P SingKuangTan 在本文中，我们证明了非确定性多项式时间复杂度（NP）不等于多项式时间复杂度（P）。我们开发了布尔代数，它将推断非确定性多项式计算时间马尔可夫随机场的两个变量的解。我们证明了无论我们如何简化布尔代数，它都不能在多项式计算时间内运行（NP 不等于 P）。我们还开发了证明所有多项式计算时间多层布尔代数都可以转换为另一个多项式计算时间多层布尔代数，其中第一层只有“非”运算。所以在简化布尔代数的过程中，我们只需要考虑在第一层只假设'非'操作的因式分解操作。我们还为马尔可夫随机场链和以马尔可夫随机场形式表示的 2sat 问题开发了多项式计算时间布尔代数，以给出多项式计算时间马尔可夫随机场的示例。功夫计算机科学, 几何复杂性理论 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefnpvspexplainchinese-210806165415-thumbnail.jpg?width=120&height=120&fit=bounds" /> 在本文中，我们证明了非确定性多项式时间复杂度（NP）不等于多项式时间复杂度（P）。我们开发了布尔代数，它将推断非确定性多项式计算时间马尔可夫随机场的两个变量的解。我们证明了无论我们如何简化布尔代数，它都不能在多项式计算时间内运行（NP 不等于 P）。我们还开发了证明所有多项式计算时间多层布尔代数都可以转换为另一个多项式计算时间多层布尔代数，其中第一层只有“非”运算。所以在简化布尔代数的过程中，我们只需要考虑在第一层只假设'非'操作的因式分解操作。我们还为马尔可夫随机场链和以马尔可夫随机场形式表示的 2sat 问题开发了多项式计算时间布尔代数，以给出多项式计算时间马尔可夫随机场的示例。功夫计算机科学, 几何复杂性理论

NP vs P 的简要说明。使用马尔可夫随机场和布尔代数简化证明 Np 不等于 P from Sing Kuang Tan

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0 Hang Seng Index 8 trends /slideshow/hang-seng-index-8-trends/249853327 hangseng8trends-210725091835
Use Clustering to extract 8 trends from Hang Seng Chart. Kung Fu Computer Science, Geometric complexity theory]]>
Use Clustering to extract 8 trends from Hang Seng Chart. Kung Fu Computer Science, Geometric complexity theory]]> Sun, 25 Jul 2021 09:18:35 GMT /slideshow/hang-seng-index-8-trends/249853327 SingKuangTan@slideshare.net(SingKuangTan) Hang Seng Index 8 trends SingKuangTan Use Clustering to extract 8 trends from Hang Seng Chart. Kung Fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hangseng8trends-210725091835-thumbnail.jpg?width=120&height=120&fit=bounds" /> Use Clustering to extract 8 trends from Hang Seng Chart. Kung Fu Computer Science, Geometric complexity theory

Hang Seng Index 8 trends from Sing Kuang Tan

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0 Mathematical Proof types /slideshow/mathematical-proof-types/249735354 prooftypes-210714095508
Describe 4 types of Mathematical Proof: Algebraic Proof, Visual Proof, Logic Proof, Algorithmic Proof. Kung Fu Computer Science, Geometric complexity theory]]>
Describe 4 types of Mathematical Proof: Algebraic Proof, Visual Proof, Logic Proof, Algorithmic Proof. Kung Fu Computer Science, Geometric complexity theory]]> Wed, 14 Jul 2021 09:55:08 GMT /slideshow/mathematical-proof-types/249735354 SingKuangTan@slideshare.net(SingKuangTan) Mathematical Proof types SingKuangTan Describe 4 types of Mathematical Proof: Algebraic Proof, Visual Proof, Logic Proof, Algorithmic Proof. Kung Fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/prooftypes-210714095508-thumbnail.jpg?width=120&height=120&fit=bounds" /> Describe 4 types of Mathematical Proof: Algebraic Proof, Visual Proof, Logic Proof, Algorithmic Proof. Kung Fu Computer Science, Geometric complexity theory

Mathematical Proof types from Sing Kuang Tan

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0 Brief explanation of NP vs P. Prove Np not equal P using Markov Random Field and Boolean Algebra Simplification /slideshow/brief-np-vspexplain-249524831/249524831 briefnpvspexplain-210629055704
In this paper, we proved that Non-deterministic Polynomial time complexity (NP) is not equal to Polynomial time complexity (P). We developed the Boolean algebra that will infer the solution of two variables of a Non-deterministic Polynomial computation time Markov Random Field. We showed that no matter how we simplified the Boolean algebra, it can never run in Polynomial computation time (NP not equal to P). We also developed proof that all Polynomial computation time multi-layer Boolean algebra can be transformed to another Polynomial computation time multi-layer Boolean algebra where there are only 'Not' operations in the first layer. So in the process of simplifying the Boolean algebra, we only need to consider factorization operations that only assumes only 'Not' operations in the first layer. We also developed Polynomial computation time Boolean algebra for Markov Random Field Chain and 2sat problem represented in Markov Random Field form to give examples of Polynomial computation time Markov Random Field. Kung Fu Computer Science, Geometric complexity theory]]>
In this paper, we proved that Non-deterministic Polynomial time complexity (NP) is not equal to Polynomial time complexity (P). We developed the Boolean algebra that will infer the solution of two variables of a Non-deterministic Polynomial computation time Markov Random Field. We showed that no matter how we simplified the Boolean algebra, it can never run in Polynomial computation time (NP not equal to P). We also developed proof that all Polynomial computation time multi-layer Boolean algebra can be transformed to another Polynomial computation time multi-layer Boolean algebra where there are only 'Not' operations in the first layer. So in the process of simplifying the Boolean algebra, we only need to consider factorization operations that only assumes only 'Not' operations in the first layer. We also developed Polynomial computation time Boolean algebra for Markov Random Field Chain and 2sat problem represented in Markov Random Field form to give examples of Polynomial computation time Markov Random Field. Kung Fu Computer Science, Geometric complexity theory]]> Tue, 29 Jun 2021 05:57:04 GMT /slideshow/brief-np-vspexplain-249524831/249524831 SingKuangTan@slideshare.net(SingKuangTan) Brief explanation of NP vs P. Prove Np not equal P using Markov Random Field and Boolean Algebra Simplification SingKuangTan In this paper, we proved that Non-deterministic Polynomial time complexity (NP) is not equal to Polynomial time complexity (P). We developed the Boolean algebra that will infer the solution of two variables of a Non-deterministic Polynomial computation time Markov Random Field. We showed that no matter how we simplified the Boolean algebra, it can never run in Polynomial computation time (NP not equal to P). We also developed proof that all Polynomial computation time multi-layer Boolean algebra can be transformed to another Polynomial computation time multi-layer Boolean algebra where there are only 'Not' operations in the first layer. So in the process of simplifying the Boolean algebra, we only need to consider factorization operations that only assumes only 'Not' operations in the first layer. We also developed Polynomial computation time Boolean algebra for Markov Random Field Chain and 2sat problem represented in Markov Random Field form to give examples of Polynomial computation time Markov Random Field. Kung Fu Computer Science, Geometric complexity theory <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/briefnpvspexplain-210629055704-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this paper, we proved that Non-deterministic Polynomial time complexity (NP) is not equal to Polynomial time complexity (P). We developed the Boolean algebra that will infer the solution of two variables of a Non-deterministic Polynomial computation time Markov Random Field. We showed that no matter how we simplified the Boolean algebra, it can never run in Polynomial computation time (NP not equal to P). We also developed proof that all Polynomial computation time multi-layer Boolean algebra can be transformed to another Polynomial computation time multi-layer Boolean algebra where there are only 'Not' operations in the first layer. So in the process of simplifying the Boolean algebra, we only need to consider factorization operations that only assumes only 'Not' operations in the first layer. We also developed Polynomial computation time Boolean algebra for Markov Random Field Chain and 2sat problem represented in Markov Random Field form to give examples of Polynomial computation time Markov Random Field. Kung Fu Computer Science, Geometric complexity theory

Brief explanation of NP vs P. Prove Np not equal P using Markov Random Field and Boolean Algebra Simplification from Sing Kuang Tan

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0 https://cdn.slidesharecdn.com/profile-photo-SingKuangTan-48x48.jpg?cb=1750770724 Use machine learning to solve problems Face recognition, Pedestrian detection, Object counting Bayesian modeling, Programming, Convex optimization sites.google.com/view/singkuangtan/home https://cdn.slidesharecdn.com/ss_thumbnails/neuronneveroverfits-230222093301-3afbc80f-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/neuronneveroverfitspptx/256012744 neuron_never_overfits.... https://cdn.slidesharecdn.com/ss_thumbnails/sqrpegprobtestfn-220529081831-ea382bc6-thumbnail.jpg?width=320&height=320&fit=bounds SingKuangTan/sqrpegprobtestfnpptx Square Peg Problem Tes... https://cdn.slidesharecdn.com/ss_thumbnails/convsqrpegprob-220315121559-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/conv-sqr-pegprob/251353026 Convex Square Peg Problem