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1 габариты
1) Naval: limiting dimensions2) Engineering: size3) Mathematics: over-all dimensions (of)4) Economy: dimension5) Mining: outline6) Electronics: geometry, gross geometry7) Information technology: dimension of array, outline dimension (= outside dimensions), outside dimensions8) Oil: overall dimensions9) Astronautics: envelope10) Business: dimensions11) Sakhalin energy glossary: space (например, to minimize space)12) Makarov: external dimensions13) Logistics: material bulk -
2 вектор
one-dimensional array, array, ( на комплексной плоскости) phasor, vector* * *ве́ктор м.
vectorно́рма ве́ктора — length [norm] of a vectorпрое́кция [составля́ющая] ве́ктора — vector componentаксиа́льный ве́ктор — axial vector, pseudovectorба́зисный ве́ктор — basis vectorбезвихрево́й ве́ктор — irrotational vectorбесконечноме́рный ве́ктор — vector of infinite number of dimensions, dimensional vectorве́ктор Бю́ргерса — Burgers [slip] vectorвзаи́мные ве́кторы — reciprocal vectorsве́ктор возду́шной ско́рости — heading velocity, air speed [heading] vectorволново́й ве́ктор — wave vectorвраща́ющийся ве́ктор — rotating vectorдвойно́й ве́ктор — divectorедини́чный ве́ктор — unit vectorковариа́нтный ве́ктор — covariant vectorколлинеа́рные ве́кторы — collinear vectorsкомплана́рные ве́кторы — coplanar vectorsконтравариа́нтный ве́ктор — contravariant vectorкоордина́тный ве́ктор — position vectorве́ктор кривизны́ — buckling vectorлине́йно-зави́симые ве́кторы — linear-dependent vectorsлине́йно-незави́симые ве́кторы — linear-independent vectorsмагни́тный ве́ктор — magnetic vectorn-ме́рный ве́ктор — n -dimensional vectorненулево́й ве́ктор — non-vanishing vectorве́ктор норма́ли — normal vectorнулево́й ве́ктор — null vectorобращё́нный ве́ктор — reversed vectorортогона́льные ве́кторы — orthogonal vectorsосево́й ве́ктор — axial vector, pseudovectorве́ктор По́йнтинга — Pounting's [energy-flux] vectorве́ктор положе́ния то́чки — radius vectorполя́рный ве́ктор — polar vectorпростра́нственный ве́ктор — space vectorве́ктор путево́й ско́рости — track velocity, ground-speed [track] vectorра́вные ве́кторы — equipollent vectorsрезульти́рующий ве́ктор — resultant vectorсвобо́дный ве́ктор — free vectorсвя́занный ве́ктор — localized vectorве́ктор сдви́га — Burgers [slip] vectorсимволи́ческий ве́ктор — symbolic vectorве́ктор скольже́ния — Burgers [slip] vectorскользя́щий ве́ктор — non-localized vectorве́ктор ско́рости — velocity vectorсвя́занный ве́ктор — localized vectorсо́бственный ве́ктор — eigenvector, proper [latent] vectorсоленоида́льный ве́ктор — solenoidal vectorсоставля́ющий ве́ктор — component vectorве́ктор состоя́ния — state vectorтангенциа́льный ве́ктор — tangent vectorве́ктор то́ка — current vectorуправля́ющий ве́ктор — control vectorэлектри́ческий ве́ктор — electric vector* * * -
3 размерность
1. ж. dimensions of a quantity2. ж. dimensionality"проклятие размерности" — curse of dimensionality
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4 габаритное поле
1) Computers: dimension2) Engineering: dimension (в графопостроителях)3) Information technology: dimensions (в графопостроителях), dimension of array -
5 Определенные артикли перед существительными, которые снабжены ссылками
The differential problem (1) can be reduced to the form (2)The asymptotic formula (1) follows from the above lemmaThe differential equation (1) can be solved numericallyWhat is needed in the final result is a simple bound on quantities of the form (1)The inequality (1) (артикль можно опустить) shows that $a>b$The bound (estimate) (2) is not quite as good as the bound (estimate) (1)If the norm of $A$ satisfies the restriction (1), then by the estimate (2) this term is less than unitySince the spectral radius of $A$ belongs to the region (1), this iterative method converges for any initial guessesThe array (1) is called the matrix representing the linear transformation of $f$It should be noted that the approximate inequality (1) bounds only the absolute error in $x$The inequality (1) shows that...The second step in our analysis is to substitute the forms (1) and (2) into this equation and simplify it by dropping higher-order termsFor small $ze$ the approximation (1) is very good indeedA matrix of the form (1), in which some eigenvalue appears in more than one block, is called a derogatory matrixThe relation between limits and norms is suggested by the equivalence (1)For this reason the matrix norm (1) is seldom encountered in the literatureTo establish the inequality (1) from the definition (2)Our conclusion agrees with the estimate (1)The characterization is established in almost the same way as the results of Theorem 1, except that the relations (1) and (2) take place in the eigenvalue-eigenvector relation...This vector satisfies the differential equation (1)The Euclidean vector norm (2) satisfies the properties (1)The bound (1) ensures only that these elements are small compared with the largest element of $A$There is some terminology associated with the system (1) and the matrix equation (2)A unique solution expressible in the form (1) restricts the dimensions of $A$The factorization (1) is called the $LU$-factorizationIt is very uncommon for the condition (1) to be violatedThe relation (1) guarantees that the computed solution gives very small residualThis conclusion follows from the assumptions (1) and (2)The factor (1) introduced in relation (2) is now equal to 2The inequalities (1) are still adequateWe use this result without explicitly referring to the restriction (1)Русско-английский словарь по прикладной математике и механике > Определенные артикли перед существительными, которые снабжены ссылками
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6 мультипликативная размерность
Русско-английский научный словарь > мультипликативная размерность
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7 мультипликативная размерность
Русско-английский военно-политический словарь > мультипликативная размерность
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8 размерность
Русско-английский словарь по информационным технологиям > размерность
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