Overview
Chechipiri-odha optimization inoshandisa curvature ruzivo (iyo yeHessian matrix yechipiri inotorwa) kutora nhanho dzakangwara kuenda kune hushoma, kwete kutsetseka chete. Inogona kuchinjika mune yakaderera iterations pane yakajeka gradient kudzika, asi mutengo wekombuta curvature unoita hunyengeri kuyera.
Yechipiri-Odha Optimization uye Newton Methods inyanzvi yekuvaka inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero.
Deep Dive
Gradient descent inongoziva mutsetse panzvimbo yako yazvino, saka inotora saizi yakamisikidzwa kana yakarongedzwa neruoko uye inotarisira zvakanaka. Nzira yaNewton inoenderera mberi: inotarisawo kuti kutsetseka kuri kuchinja sei ( curvature ), yakatorwa neHessian, matrix ezvimwe zvikamu zvechipiri. Iyo yekuvandudza inowanza iyo yakatenderedza Hessian neiyo gradient, iyo inoereketa yega yega nzira uye inogara padhuze nehushoma hwepanzvimbo quadratic approximation. Kuti uwane mbiya yequadratic yakakwana, nzira yaNewton inosvika pasi nenhanho imwe chete. Kubata kwacho kune hutsinye: modhi ine N paramita ine N-by-N Hessian, saka kuchengetedza uye kuchinjisa kunodhura zvinosvika N-squared memory uye N-cubed compute. Kune mabhiriyoni-parameter network izvo hazvigoneke, ndosaka varapi vachishandisa zvakachipa approximations.
Technical Insight
Iyo yakakosha Newton update ndeye x_new = x - H_inverse nguva gradient, uko H ari Hessian. Nzira dzeQuasi-Newton dzakaita seBFGS neL-BFGS dzinodzivirira komputa H zvakananga nekuvaka fungidziro inomhanya yekupesana kwayo kubva kune inoteedzana gradient misiyano. L-BFGS inongochengeta chete mashoma ekupedzisira gradient uye nhanho mavheji panzvimbo yeiyo yakazara matrix, ichicheka ndangariro kubva kuN-yakapetwa kusvika kune diki yakapetwa yeN uku ichichengeta yakawanda ye convergence kukurumidza.
Kubata Chechipiri-Odha Optimization uye Newton Nzira
Chechipiri-odha optimization inoshandisa curvature ruzivo (iyo yeHessian matrix yechipiri inotorwa) kutora nhanho dzakangwara kuenda kune hushoma, kwete kutsetseka chete. Inogona kuchinjika mune yakaderera iterations pane yakajeka gradient kudzika, asi mutengo wekombuta curvature unoita hunyengeri kuyera. Yechipiri-Odha Optimization uye Newton Methods inyanzvi yekuvaka inobata mhando yemhando, mutengo wezvivakwa, latency, uye kuvimbika pachiyero. Kuvaka kunzwisisa kwakadzama, kubata Second-Order Optimization uye Newton Methods semuenzaniso wekushanda, kwete chinhu chimwe chete: tsanangura zvinodikanwa, kujekesa fungidziro, uye patsanura izvo system inogona kuita yakavimbika kubva kune ichiri kuda nyanzvi kutonga.
Mukuita, zvikwata zvakasimba zvinoshandisa Second-Order Optimization uye Newton Methods inokwirisa zvivakwa, data, uye sarudzo dzezvivakwa zvinopesana nekuvimbika uye mutengo. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Panguva imwecheteyo, Kukwirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore.
Zvisarudzo zvezvivakwa zvinotyaira kuita uye mutengo wekushandisa kwemakore. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete.
Dzidzo yehunyanzvi inobatsira zvikwata kusarudza murwi wakakodzera, kwete iwo mutsva chete. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira.
Sarudzo dzeinjiniya dziri nani dzinoderedza zviitiko zvekuvimbika mukugadzira. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
L-BFGS inokodzera kudzoreredza uye mamwe maconvex modhi mu scikit-dzidza, uko inowanzo rova plain gradient kudzika pamadiki kusvika epakati dataset.
Bundle gadziriso mu 3D kuvakazve uye SLAM, uko Gauss-Newton uye Levenberg-Marquardt inonatsa kamera inomira uye inonongedza nzvimbo.
Kudzidzisa diki fizikisi-inoziva neural network uko L-BFGS inowana chaiyo iyo Adhamu anotamburira kusvika.
Shampoo uye K-FAC inomhanyisa kudzidziswa kwakadzama kwakadzama nekuenzanisa chimiro cheHessian.
Maitiro Ekuita
Yechipiri-Odha Optimization uye Newton Nzira mukuita
L-BFGS inokodzera kudzoreredzwa kwemaitiro uye mamwe mamodhekisi emhando mune scikit-dzidza, uko inowanzorova plain gradient descent pamadiki kusvika epakati dataset.
L-BFGS inokodzera kugadzirisa regression uye mamwe maconvex modhi mu scikit-dzidza, uko inowanzorova pachena gradient kudzika pamadiki kusvika epakati datasets Zvikwata zvinowanzowana mibairo iri nani kana vatsanangura hunhu hwepamberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Yechipiri-Odha Optimization uye Newton Nzira mukuita
Bundle kugadziridzwa mu 3D kuvakazve uye SLAM, uko Gauss-Newton uye Levenberg-Marquardt inonatsa kamera inomira uye inonongedza nzvimbo.
Kugadziriswa kweBundle mukuvakazve kwe3D uye SLAM, uko Gauss-Newton naLevenberg-Marquardt vanonatsa kamera inomira uye nzvimbo dzemapoka Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Yechipiri-Odha Optimization uye Newton Nzira mukuita
Kudzidzira diki diki-ruzivo neural network uko L-BFGS inowana chaiyo iyo Adhamu anotamburira kusvika.
Kudzidzisa madiki efizikisi-anoziva neural network uko L-BFGS inowana chaiyo iyo Adhama anotamburira kusvika Matimu anowanzo kuwana mhedzisiro iri nani kana ivo vachitsanangudza hunhu kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Yechipiri-Odha Optimization uye Newton Nzira mukuita
Shampoo neK-FAC inomhanyisa kudzidziswa kwakadzama kwakadzama nekuenzanisa chimiro cheHessian.
Shampoo neK-FAC inomhanyisa kudzidziswa kwakadzama kwakadzama nekuenzanisa maumbirwo eHessian Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura zvikumbaridzo zvemhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa nemitengo yekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
Kugadzirisa imwe bhenji kunogona kuvanza yakafara system kushaya simba.
Infrastructure uye mari yekugadzirisa inowanzotarisirwa pasi.
Chengetedzo uye kucherechedzwa mapundu anogona kukura sezvo masisitimu anowedzera kuoma.
Implementation Roadmap
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa.
Tsanangura latency, mhando, uye mutengo zvinangwa usati waitwa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Benchmark pasi pechokwadi mutoro uye data mamiriro.
Benchmark pasi pechokwadi mutoro uye data mamiriro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro.
Chishandiso chekutarisa zvikanganiso, kudonha, uye mushandisi maitiro. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera.
Gadzirira nzira dzekudzosera kumashure uye dzezviitiko usati wawedzera. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.