Hacker NewsA Recursive Algorithm to Render Signed Distance Fields
https://pointersgonewild.com/2026-03-06-a-recursive-algorithm-to-render-signed-distance-fields/
#HackerNews #ARecursiveAlgorithm #RenderSignedDistanceFields #ComputerGraphics #AlgorithmDesign
Jacky KapadiaAlgorithm-Forged: The Nano-Architected Meta-Material Rivaling Steel’s Might.
#MetaMaterials #NanoArchitected #FutureOfMaterials #LightweightStrong #AdvancedMaterials #MaterialScience #SteelRival #AlgorithmDesign #Nanotechnology #AerospaceInnovation
The Kernighan-Lin Search Algorithm — https://arxiv.org/abs/2502.00316
#HackerNews #KernighanLinAlgorithm #SearchAlgorithm #ComputerScience #Optimization #Techniques #AlgorithmDesign
#HackerNews #KernighanLinAlgorithm #SearchAlgorithm #ComputerScience #Optimization #Techniques #AlgorithmDesign
The Kernighan-Lin Search Algorithm
The traveling salesman problem (TSP) and the graph partitioning problem (GPP) are two important combinatorial optimization problems with many applications. Due to the NP-hardness of these problems, heuristic algorithms are commonly used to find good, or hopefully near-optimal, solutions. Kernighan and Lin have proposed two of the most successful heuristic algorithms for these problems: The Lin-Kernighan (LK) algorithm for TSP and the Kernighan-Lin (KL) algorithm for GPP. Although these algorithms are problem specific to TSP and GPP, they share a problem-agnostic mechanism, called variable depth search, that has wide applicability for general search. This paper expresses this mechanism as part of a general search algorithm, called the Kernighan-Lin Search algorithm, to facilitate its use beyond the TSP and GPP problems. Experimental comparisons with other general search algorithms, namely, genetic algorithms, hill climbing, and simulated annealing, on function optimization test suites confirm that the new algorithm is very successful in solution quality and running time.
zartomLearn how to build a fair & efficient Lottery Number Generator in Python! Go beyond basic random number generation & explore advanced techniques for unique number selection, weighted probabilities, and secure random number generation. LotteryNumberGenerator #PythonCoding #RandomNumbers #AlgorithmDesign #ProgrammingTips #SecureRandom
https://tech-champion.com/mathematics/secure-lottery-number-generator-in-python-avoiding-duplicate-numbers
https://tech-champion.com/mathematics/secure-lottery-number-generator-in-python-avoiding-duplicate-numbers
CerYoVon meinem Projekt #ANN_at_work sind die Arbeiten #bias&bias und "Hunt2Make" vom #38c3 art- Team ausgewählt worden (Details im Projektpage-Link im Profil). Ich freue mich total, dabei sei zu dürfen!
Außerdem gebe ich beim Kongress den #stablediffusion -Workshop "KI.VooDoo". Die Zeiten sind noch nicht klar. Meldet Euch gern, wenn ihr Interesse habt. Zeiten gebe ich bekannt, sobald ich sie habe.
#unmask_ai #berlin #veranstaltungen_38c3 #haecksenassembly #mediaartist #algorithmdesign #diffusion_models #step2take
Mohammad HajiaghayiIn our effort to put courses online, we continue lectures on Algorithmic Lower Bound Course. Now you can watch
Lesson 4-11: Algorithmic Lower Bounds by Mohammad Hajiaghayi - NP-Completeness and Beyond
(FEEL FREE TO SUBSCRIBE TO YOUTUBE @hajiaghayi FOR FUTURE LESSONS Premiering on WEDNESDAYS)
https://youtu.be/VZyffnAb1r0 (Lesson 4: 3-Partition Problem & Proving NP-Hardness)
https://youtu.be/4fCD9_1eQw0 (Lesson 5: Puzzle Problem NP-Hardness & 3-Partition)
https://youtu.be/FIyEj72-UJQ (Lesson 6: 3-SAT Problem & Proving NP-Hardness)
https://youtu.be/tbSJzaKx2pA (Lesson 7: Puzzle Problem NP-Hardness via 3-SAT)
https://youtu.be/voRVebBsh94 (Lesson 8: Fine-grained Subcubic Complexity: Part 1)
https://youtu.be/gRURSM6QARo (Lesson 9: Fine-grained Subcubic Complexity: Part 2)
https://youtu.be/qPw82bTAXkc (Lesson 10: Fine-grained Subquadratic Complexity 1)
https://youtu.be/C6j4avVkI7U (Lesson 11: Fine-grained Subquadratic Complexity 2)
#NP,
#NonDeterministicSpace, hashtag
#CommunicationComplexity, hashtag
For comprehensive handwritten lecture notes on this course, visit the instructor's website:
http://www.cs.umd.edu/~hajiagha/
The course textbook "Computational Intractability: A Guide to Algorithmic Lower Bounds" by Demaine, Gasarch, and Hajiaghayi is available for free at:
Lesson 4: Algorithmic Lower Bounds by Mohammad Hajiaghayi: 3-Partition Problem & Proving NP-Hardness
Austin (=> @@abstractcow Speaking of #AlgorithmDesign (which I’ve taken as a course a year ago, so kinda late to ask), any tricks for proving NP-completeness? It’s quite difficult to find a problem to reduce to...
BasicsComphttps://basicscomp.com/basic-introduction-to-coding-and-programming/
#codingbasics #programmingconcepts #technologyeducation #codingforbeginners #programmingskills #algorithmdesign #datastructures #objectorientedprogramming #debuggingtechniques #softwaredevelopmentmethodologies #learntocode #programminglanguages #continuouslearning #technologycareer #techsavvy
Thomas Otto (he/him)Using sensitive data in #ml systems to fight #discrimination? Since other factors can still re-create social Inequality without them. To counteract these effects, create systems with transparent factors through well-trained teams. #algorithmdesign #design
https://hbr.org/2023/03/removing-demographic-data-can-make-ai-discrimination-worse
https://hbr.org/2023/03/removing-demographic-data-can-make-ai-discrimination-worse
Removing Demographic Data Can Make AI Discrimination Worse
A recent study suggests that denying AI decision makers access to sensitive data actually increases the risks of discriminatory outcome. That’s because the AI draws incomplete inferences from the data or partially substitutes by identifying proxies. Providing sensitive data would eliminate this problem, but it is problematic to do so in certain jurisdictions. The authors present work-arounds that may answer the problem in some countries.