Pengguna:Dedhert.Jr/Uji halaman 01/22: Perbedaan antara revisi

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Dedhert.Jr (bicara | kontrib)
Dedhert.Jr (bicara | kontrib)
 
(2 revisi perantara oleh pengguna yang sama tidak ditampilkan)
Baris 38:
Adapun [[pertidaksamaan Ptolemaus]], sebuah sifat lain yang melibatkan jarak Euklides di antara empat titik {{math|1=''p''}}, {{math|1=''q''}}, {{math|1=''r''}}, dan {{math|1=''s''}}, yang menyatakan bahwa
<math display=block>d(p,q)\cdot d(r,s)+d(q,r)\cdot d(p,s)\ge d(p,r)\cdot d(q,s).</math>
Untuk titik-titik di bidang tersebut, rumus di atas dapat dikatakan bahwa untuk setiap [[segi empat]], perkalian antara sisi yang berhadapan dari jumlah segi empat lebih besar dari perkalian dari sisi diagonalnya. Akan tetapi, pertidaksamaan Ptolemaus lebih umumnya berlaku untuk titik-titik yang ada di ruang Euklides untuk setiap dimensi, tidak peduli bentuk susunannya.<ref>{{citation|title=Rays, Waves, and Scattering: Topics in Classical Mathematical Physics|series=Princeton Series in Applied Mathematics|first=John A.|last=Adam|publisher=Princeton University Press|year=2017|isbn=978-1-4008-8540-4|pages=26–27|chapter-url=https://books.google.com/books?id=DnygDgAAQBAJ&pg=PA26|chapter=Chapter 2. Introduction to the "Physics" of Rays|doi=10.1515/9781400885404-004}}</ref> Untuk titik-titik di ruang metrik yang bukan ruang Euklides, pertidaksamaan ini tidak berlaku benar. [[Geometri jarak]] Euklides mempelajari sifat-sifat dari jarak Euklides seperti pertidaksamaan Ptolemaus, andserta mempunyai penerapan yang menentukan himpunan yang diberikan dari jarak yang dimulai dari titik di ruang Euklides.<ref>{{citation|title=Euclidean Distance Geometry: An Introduction|series=Springer Undergraduate Texts in Mathematics and Technology|first1=Leo|last1=Liberti|first2=Carlile|last2=Lavor|publisher=Springer|year=2017|isbn=978-3-319-60792-4|page=xi|url=https://books.google.com/books?id=jOQ2DwAAQBAJ&pg=PP10}}</ref>
 
== Jarak Euklides kuadrat ==
{{multiple image
| image1 = 3d-function-5.svg
| caption1 =A Sebuah [[conekerucut]], theyang dibentuk dari [[GraphGrafik of a functionfungsis|graphgrafik]] of Euclideandari distancejarak fromEuklides thedari origintitik inasal thedi planebidang.
| image2 = 3d-function-2.svg
| caption2 =A Sebuah [[paraboloid]], theyang dibentuk dari graphgrafik ofdari squaredjarak EuclideanEuklides distancekuadrat fromdari thetitik originasal
}}
 
Baris 52:
<math display=block>d^2(p,q) = (p_1 - q_1)^2 + (p_2 - q_2)^2+\cdots+(p_i - q_i)^2+\cdots+(p_n - q_n)^2.</math>
 
Selain penerapannya dalam membandingkan jarak, jarak Euklides kuadrat merupakan alat penting di bidang [[statistika]],. yangJarak tersebut dipakai dalam metode [[kuadrat terkecil]], asebuah metode statistik standardpenyuaian (''method of fitting statistical'') estimatesyang tomengestimasi data bydengan minimizingmeminimum thererata averagedari ofjarak thekuadrat squareddi distancesantara betweennilai observedyang anddiamati estimateddan valuesnilai yang diestimasi,<ref>{{citation|title=Basic Statistics in Multivariate Analysis|series=Pocket Guide to Social Work Research Methods|first1=Karen A.|last1=Randolph|author1-link=Karen Randolph|first2=Laura L.|last2=Myers|publisher=Oxford University Press|year=2013|isbn=978-0-19-976404-4|page=116|url=https://books.google.com/books?id=WgSnudjEsrMC&pg=PA116}}</ref> anddan asdipakai thesebagai simplest form ofbentuk [[divergenceDeret divergen|divergensi]] tosederhana untuk comparemembandingkan [[probabilitydistribusi distributionprobabilitas]]s.<ref>{{citation
| last = Csiszár | first = I. | author-link = Imre Csiszár
| doi = 10.1214/aop/1176996454
Baris 61:
| title = {{mvar|I}}-divergence geometry of probability distributions and minimization problems
| volume = 3
| year = 1975}}</ref> The addition of squared distances to each other, as is done in least squares fitting, corresponds to an operation on (unsquared) distances called [[Pythagorean addition]].<ref>{{citation |author=Moler, Cleve and Donald Morrison |title=Replacing Square Roots by Pythagorean Sums |journal=IBM Journal of Research and Development |volume=27 |issue=6 |pages=577–581 |year=1983 |url=http://www.research.ibm.com/journal/rd/276/ibmrd2706P.pdf |doi=10.1147/rd.276.0577 | citeseerx = 10.1.1.90.5651 }}</ref> InDalam [[clusteranalisis analysiskluster]], squaredjarak distanceskuadrat candapat bedipakai useduntuk tomemperkuat strengthenefek thedari effectjarak ofyang longerlebih distancespanjang.<ref name=spencer>{{citation|title=Essentials of Multivariate Data Analysis|first=Neil H.|last=Spencer|publisher=CRC Press|year=2013|isbn=978-1-4665-8479-2|contribution=5.4.5 Squared Euclidean Distances|page=95|contribution-url=https://books.google.com/books?id=EG3SBQAAQBAJ&pg=PA95}}</ref>
 
SquaredJarak EuclideanEuklides distancekuadrat doestidak notmembentuk formruang a metric spacemetrik, as it does notsebab satisfytidak thememenuhi triangleketaksamaan inequalitysegitiga.<ref>{{citation|last1=Mielke|first1=Paul W.|last2=Berry|first2=Kenneth J.|editor1-last=Brown|editor1-first=Timothy J.|editor2-last=Mielke|editor2-first=Paul W. Jr.|contribution=Euclidean distance based permutation methods in atmospheric science|doi=10.1007/978-1-4757-6581-6_2|pages=7–27|publisher=Springer|title=Statistical Mining and Data Visualization in Atmospheric Sciences|year=2000}}</ref> HoweverAkan ittetapi, isjaraknya abersifat smoothmulus, strictlyyakni berupa [[convexfungsi functioncembung]] ofsempurna thedari twodua pointstitik, unliketidak theseperti distance,jarak whichbiasa isyang non-smoothtidak mulus (nearmendekati pairspasangan ofdari equaltitik pointsyang sama) anddan convexcembung but(tetapi notbukan strictlycembung convexsempurna). TheKarena squareditu, distancejarak isEuklides thuskuadrat preferredseringkali indipakai dalam [[optimizationteori theoryoptimisasi]], sincesebab itjarak allowstersebut memungkinkan pemakaian [[convexanalisis analysiscembung]]. toSelain beitu, used.karena Sincepenguadratan squaringmerupakan isfungsi amonotonik [[monotonicdari function]] ofnilai non-negative valuesnegatif, minimizingmaka squaredpeminimuman distancejarak iskuadrat equivalentekuivalen todengan minimizingpeminimuman thejarak Euclidean distanceEuklides, sosehingga themasalah optimizationoptimisasi problemjuga isekuivalen equivalentdengan in termsmasalah ofyang eithersama, buttetapi easierpenyelesaiannya tomenjadi solvelebih usingmudah squaredketika distancememakai jarak kuadrat.<ref>{{citation|title=Maxima and Minima with Applications: Practical Optimization and Duality|volume=51|series=Wiley Series in Discrete Mathematics and Optimization|first=Wilfred|last=Kaplan|publisher=John Wiley & Sons|year=2011|isbn=978-1-118-03104-9|page=61|url=https://books.google.com/books?id=bAo6KNZcUP0C&pg=PA61}}</ref>
 
TheKumpulan collectiondari ofsemua alljarak squaredkuadrat distancesdi betweenantara pairspasangan oftitik pointsdari fromhimpunan aterhingga finitedapat setdisimpan maydalam be stored in asebuah [[Euclideanmatriks distancejarak matrixEuklides]], and isserta useddipakai inke thisbentuk formtersebut indalam distancegeometri geometryjarak.<ref>{{citation|title=Euclidean Distance Matrices and Their Applications in Rigidity Theory|first=Abdo Y.|last=Alfakih|publisher=Springer|year=2018|isbn=978-3-319-97846-8|page=51|url=https://books.google.com/books?id=woJyDwAAQBAJ&pg=PA51}}</ref>
 
== Perumuman ==
Dalam cabang matematika lebih lanjut, saat jarak Euklides dipandang sebagai [[ruang vektor]], jaraknya diiringi dengan [[Norma (matematika)|norma]] yang disebut sebagai [[Norma (matematika)#Norma Euklides|norma Euklides]], didefinisikan sebagai jarak dari masing-masing vektor yang berawal dari [[Titik asal (matematika)|titik asal]]. One of the important properties of this norm, relative to other norms, is that it remains unchanged under arbitrary rotations of space around the origin.<ref>{{citation|title=Relativistic Celestial Mechanics of the Solar System|first1=Sergei|last1=Kopeikin|first2=Michael|last2=Efroimsky|first3=George|last3=Kaplan|publisher=John Wiley & Sons|year=2011|isbn=978-3-527-63457-6|page=106|url=https://books.google.com/books?id=uN5_DQWSR14C&pg=PA106}}</ref> Menurut [[teorema Dvoretzky]], setiap [[ruang vektor bernorma]] dimensi terhingga mempunyai subruang dimensi tinggi dengan norma yang kira-kira dekat dengan norma Euklides, satu-satunya norma yang ada di sifat tersebut.<ref>{{citation|last=Matoušek|first=Jiří|author-link=Jiří Matoušek (mathematician)|isbn=978-0-387-95373-1|page=349|publisher=Springer|series=[[Graduate Texts in Mathematics]]|title=Lectures on Discrete Geometry|url=https://books.google.com/books?id=K0fhBwAAQBAJ&pg=PA349|year=2002}}</ref> Jarak Euklides dapat diperluas ke ruang vektor berdimensi tak terhingga sebagai [[Norma L2|norma L<sup>2</sup>]].<ref>{{citation|title=Linear and Nonlinear Functional Analysis with Applications|first=Philippe G.|last=Ciarlet|publisher=Society for Industrial and Applied Mathematics|year=2013|isbn=978-1-61197-258-0|page=173|url=https://books.google.com/books?id=AUlWAQAAQBAJ&pg=PA173}}</ref> Jarak Euklides memberikan ruang Euklides suatu struktur [[ruang topologis]], [[topologi Euklides]], dengan [[Bola pejal (matematika)|bola pejal terbuka]] (subhimpunan dari titik yang lebih sedikit daripada jarak yang berawal dari titik yang diketahui) sebagai [[Lingkungan (matematika)|lingkungannya]].<ref>{{citation|title=General Topology: An Introduction|publisher=De Gruyter|first=Tom|last=Richmond|year=2020|isbn=978-3-11-068657-9|page=32|url=https://books.google.com/books?id=jPgdEAAAQBAJ&pg=PA32}}</ref>
 
Jarak umum lainnya dalam ruang Euklides beserta ruang vektor berdimensi rendah melibatkan:<ref>{{citation|last=Klamroth|first=Kathrin|author-link=Kathrin Klamroth|contribution=Section 1.1: Norms and Metrics|doi=10.1007/0-387-22707-5_1|pages=4–6|publisher=Springer|series=Springer Series in Operations Research|title=Single-Facility Location Problems with Barriers|year=2002}}</ref>
 
* [[Jarak Chebyshev]], yang menghitung jarak yang hanya dengan mengasumsi bahwa dimensi yang sangat signifikan adalah penting.
* [[Jarak Manhattan]], yang menghitung jarak hanya dengan mengikuti arah pada sumbu.
* [[Jarak Minkowski]], suatu perumuman yang menyatukan jarak Euklides, jarak Manhattan, dan jarak Chebyshev.
 
For points on surfaces in three dimensions, the Euclidean distance should be distinguished from the [[geodesic]] distance, the length of a shortest curve that belongs to the surface. In particular, for measuring great-circle distances on the earth or other spherical or near-spherical surfaces, distances that have been used include the [[haversine distance]] giving great-circle distances between two points on a sphere from their longitudes and latitudes, and [[Vincenty's formulae]] also known as "Vincent distance" for distance on a spheroid.<ref>{{citation|title=Computing in Geographic Information Systems|first=Narayan|last=Panigrahi|publisher=CRC Press|year=2014|isbn=978-1-4822-2314-9|contribution=12.2.4 Haversine Formula and 12.2.5 Vincenty's Formula|pages=212–214|url=https://books.google.com/books?id=kjj6AwAAQBAJ&pg=PA212}}</ref>
 
== Asal-muasal ==
Jarak Euklides adalah jarak di dalam [[ruang Euklides]] yang dinamai dari seorang matematikawan Yunani kuno yang bernama [[Euklides]]. Konsep tersebut dijelaskan dalam bukunya, [[Euclid's Elements|''Elements'']], yang menjadi buku cetak standar selama bertahun.<ref>{{citation|title=Visualization for Information Retrieval|first=Jin|last=Zhang|publisher=Springer|year=2007|isbn=978-3-540-75148-9}}</ref> Konsep tentang [[panjang]] dan [[jarak]] yang tersebar luas di seluruh budaya, kemungkinan berawal dari <u>earliest surviving "protoliterate" bureaucratic documents from [[Sumer]] in the fourth millennium BC (far before Euclid)</u>,<ref>{{citation|last=Høyrup|first=Jens|author-link=Jens Høyrup|editor1-last=Jones|editor1-first=Alexander|editor2-last=Taub|editor2-first=Liba|editor2-link=Liba Taub|contribution=Mesopotamian mathematics|contribution-url=https://akira.ruc.dk/~jensh/Publications/2018%7Bj%7D_Mesopotamian%20Mathematics_S.pdf|pages=58–72|publisher=Cambridge University Press|title=The Cambridge History of Science, Volume 1: Ancient Science|year=2018}}</ref> and have been hypothesized to develop in children earlier than the related concepts of speed and time.<ref>{{citation|last1=Acredolo|first1=Curt|last2=Schmid|first2=Jeannine|doi=10.1037/0012-1649.17.4.490|issue=4|journal=[[Developmental Psychology (journal)|Developmental Psychology]]|pages=490–493|title=The understanding of relative speeds, distances, and durations of movement|volume=17|year=1981}}</ref> But the notion of a distance, as a number defined from two points, does not actually appear in Euclid's ''Elements''. Instead, Euclid approaches this concept implicitly, through the [[Congruence (geometry)|congruence]] of line segments, through the comparison of lengths of line segments, and through the concept of [[Proportionality (mathematics)|proportionality]].<ref>{{citation|last=Henderson|first=David W.|author-link=David W. Henderson|journal=[[Bulletin of the American Mathematical Society]]|pages=563–571|title=Review of ''Geometry: Euclid and Beyond'' by Robin Hartshorne|url=https://www.ams.org/journals/bull/2002-39-04/S0273-0979-02-00949-7|volume=39|year=2002|doi=10.1090/S0273-0979-02-00949-7|doi-access=free}}</ref>
 
The [[Pythagorean theorem]] is also ancient, but it could only take its central role in the measurement of distances after the invention of [[Cartesian coordinates]] by [[René Descartes]] in 1637. The distance formula itself was first published in 1731 by [[Alexis Clairaut]].<ref>{{citation|last=Maor|first=Eli|author-link=Eli Maor|isbn=978-0-691-19688-6|pages=133–134|publisher=Princeton University Press|title=The Pythagorean Theorem: A 4,000-Year History|url=https://books.google.com/books?id=XuWZDwAAQBAJ&pg=PA133|year=2019}}</ref> Because of this formula, Euclidean distance is also sometimes called Pythagorean distance.<ref>{{citation|last1=Rankin|first1=William C.|last2=Markley|first2=Robert P.|last3=Evans|first3=Selby H.|date=March 1970|doi=10.3758/bf03210143|issue=2|journal=[[Perception & Psychophysics]]|pages=103–107|title=Pythagorean distance and the judged similarity of schematic stimuli|volume=7|s2cid=144797925|doi-access=free}}</ref> Although accurate measurements of long distances on the earth's surface, which are not Euclidean, had again been studied in many cultures since ancient times (see [[history of geodesy]]), the idea that Euclidean distance might not be the only way of measuring distances between points in mathematical spaces came even later, with the 19th-century formulation of [[non-Euclidean geometry]].<ref>{{citation|last=Milnor|first=John|author-link=John Milnor|doi=10.1090/S0273-0979-1982-14958-8|issue=1|journal=[[Bulletin of the American Mathematical Society]]|mr=634431|pages=9–24|title=Hyperbolic geometry: the first 150 years|volume=6|year=1982|doi-access=free}}</ref> The definition of the Euclidean norm and Euclidean distance for geometries of more than three dimensions also first appeared in the 19th century, in the work of [[Augustin-Louis Cauchy]].<ref>{{citation|title=Foundations of Hyperbolic Manifolds|volume=149|series=[[Graduate Texts in Mathematics]]|first=John G.|last=Ratcliffe|edition=3rd|publisher=Springer|year=2019|isbn=978-3-030-31597-9|page=32|url=https://books.google.com/books?id=yMO4DwAAQBAJ&pg=PA32}}</ref>
 
== Referensi ==
{{Reflist|colwidth=30}}