稀疏矩陣演算法 的英文怎麼說
中文拼音 [xīshūjǔzhènyǎnsuànfǎ]
稀疏矩陣演算法
英文
sparse matrix algorithm- 稀 : Ⅰ形容詞1 (事物出現得少) rare; scarce; uncommon 2 (事物之間距離遠; 空隙大) sparse; scattered 3...
- 疏 : Ⅰ動詞1 (疏通) dredge (a river etc )2 (疏忽) neglect 3 (分散; 使從密變稀) disperse; scatte...
- 矩 : 名詞1. (畫直角或正方形、矩形用的曲尺) carpenter's square; square2. (法度; 規則) rules; regulations 3. [物理學] moment
- 陣 : Ⅰ名詞1 (作戰隊伍的行列或組合方式) battle array [formation]: 布陣 deploy the troops in battle fo...
- 演 : 動詞1 (演變; 演化) develop; evolve 2 (發揮) deduce; elaborate 3 (依照程式練習或計算) drill;...
- 算 : Ⅰ動詞1 (計算數目) calculate; reckon; compute; figure 2 (計算進去) include; count 3 (謀劃;計...
- 法 : Ⅰ名詞1 (由國家制定或認可的行為規則的總稱) law 2 (方法; 方式) way; method; mode; means 3 (標...
- 稀疏 : few and scattered; few and far between; thin; sparse
- 矩陣 : [數學] matrix; array
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( 4 ) on the efficient method for the dynamical core of the new generation multi - scale forecasting model i ) we present a new multi - level sparse approximate inverse preconditnioner for the complicated 3 - d helmholtz equations in the new generation weather forecasting model. as a result, the new sparse approximate inverse preconditioned gcr and gmres algorithms are given and successfully applied in the dynamical core. numerical tests show that the new algorithms perform very efficiently, and can greatly improve the efficiency of numerical model
對此,本文提出了一種基於逐層門限技術的近似逆矩陣稀疏模式預選方法,並構造了相應的稀疏近似逆預條件子,結合gcr演算法和g州[ r衛s演算法,首次將逐層門限稀疏近似逆預條件子應用於新一代多尺度預報模式動力內核的實際計算,數值實驗表明這里給出的方法可以大大提高數值模式的計算效率。Table 2. 1. 3. further convergence property 3. 1 algoithm the convergence property of diagnal sparse quasi - newton method is proposed
另外,對hesse矩陣為稀疏的目標函數,該演算法也具有較快的收斂速度。This paper introduces the basic idea and algorithm of sparse matrix multiplication by using incompact storage method
摘要介紹了對稀疏矩陣進行壓縮存儲時,稀疏矩陣相乘運算的基本思想和演算法。For more on improving sparse matrix algorithms, read fast and effective algorithms for graph partitioning and sparse - matrix ordering
如果您想了解更多關于提高稀疏矩陣演算法的信息,請閱讀Adams solves the model by adopting lagrange dynamics equation and complementing with rigidity integ - ral algorithm and sparse matrix technology
Adams採用拉格朗日動力學方程,輔以剛性積分演算法以及稀疏矩陣技術來求解模型。The connection matrix of a network is usually large and sparse. in this article, the auther brought forward a method to reduce the connection matrix order, which was help for saving operation time and space when it is stored in computer
通過引進矩陣運算元並藉助于矩陣的分塊運算,針對網路聯絡矩陣的稀疏性,提出一種含表決系統的網路聯絡矩陣的降階方法,節約了演算法的運行時間和存儲空間。This paper applies generalized multipler method to translate convex quadratic programs with equal constraints and non - negative constraints into simple convex quadratic programs with non - negative constraints. the new algorithm is gotten by solving the simple quadratic program. it avoids the computation of inverse matrix and exploits sparsity structure in the matrix of the quadratic form. the results of numerical experiments show the effectiveness of the algorithm on large scale problems
根據廣義乘子法的思想,將具有等式約束和非負約束的凸二次規劃問題轉化為只有非負約束的簡單凸二次規劃,通過解簡單凸二次規劃來得到解等式約束和非負約束的凸二次規劃新演算法,新演算法不用求逆矩陣,這樣可充分保持矩陣的稀疏性,用來解大規模稀疏問題.數值結果表明:在微機486 / 33上就能解較大規模的凸二次規劃Within the framework of sparse bayesian learning, the algorithm extends the relevance vector machine by combining global and local kernels adaptively in the form of multiple kernels, and the improved locality preserving projection ( llp ) is then applied to reduce the column dimension of the multiple kernel input matrix to achieve less training time
在稀疏貝葉斯學習的框架下,該演算法首先以多核形式自適應結合全局核函數和局部核函數擴展相關向量機,然後應用改進的保局投影來約簡多核輸入矩陣的列維數以減少訓練時間。Association rule mining algorithms of rare matrix
稀疏矩陣的關聯規則挖掘演算法研究Different from other rank reduction methods, such as pca ( principal component analysis ) and vq ( vector quantization ), nmf ( nonnegative matrix factorization ) can get nonnegative, sparse basis vectors which make possible of the concept of a parts - based representation
與pca (主分量分析)和vq (矢量量化)等降維演算法不同, nmf (非負矩陣分解)演算法能夠分解出非負的,稀疏的特徵矩陣和編碼矩陣,能夠提取原始數據向量的局部特徵,使基於局部特徵進行分類的聚類演算法更容易實現。One is based on the discriminated matrics, the other is based on ga. we use many new ways to make the algorithms more effective, such as reducing dimension and sparsiting elements of the discriminated matrics, effectively selecting elements of the positive examples set and the counter examples set for the jointing - spreading matrics, using a new selecting operator, and so on
其次,實現了基於粗集理論的屬性約簡方法? ?基於可分辨矩陣的屬性約簡法和基於遺傳演算法的屬性約簡法,並通過降維、稀疏化、正例集和反例集的有效選取、新的選擇運算元等方法對原演算法進行了改進。Since their rediscovery, design, construction, decoding, analysis and applications of ldpc coded have become focal points of research. among them, the decoding algorithm and its implementation design are the focus of this thesis
Ldpc碼是一種具有稀疏校驗矩陣的線性分組碼,研究結果表明,採用迭代的概率譯碼演算法, ldpc碼可以達到接近香農極限的性能。This class of codes decoded with soft - in soft - out ( siso ) iterative decoding performs amazingly well. since their rediscovery, design, construction, decoding, analysis and applications of ldpc coded have become focal points of research
Ldpc碼是一種具有稀疏校驗矩陣的線性分組碼,研究結果表明,採用迭代的概率譯碼演算法, ldpc碼可以達到接近香農極限的性能。The fft of helmholtz equation on the regular domain is studied. and the multifrontal algorithm is used to solve the matrix equations in computational electromagnetics. finally, the finite - difference approximate forms of maxwell equations and the despres transmission condition are discussed
為了充分提高演算法的計算效率,研究了規則區域上helmholtz方程的fft快速演算法,以及有效地將多波前演算法引入計算電磁學領域用於求解差分稀疏矩陣方程。This one is complicated than the former. by using sparsity matrix technique and recursion, it can also be used in the larger system. the simulation system is constituted with matlab and the programs are formed with c language
前者演算法結構簡單,便於編程實現,且計算速度快,主要適用於規模較小的系統;後者演算法結構較前者復雜,程序實現困難,但由於採用了稀疏矩陣技術,並用遞推法修正因子表,所以可以應用於規模較大的系統。The holistic features are extracted by principal component analysis ( pca ), and the local features are extracted by non - negative matrix factorization with sparseness constraints ( nmfs )
首先通過主元分析演算法( pca )提取全局特徵,利用帶稀疏限制的非負矩陣分解演算法( nmfs )提取局部特徵。In this thesis, we mainly use snmf ( sparse nonnegative matrix factorization ) as the method of rank reduction, which extend the nmf to include the option to control sparseness explicitly
本文主要採用snmf (非負稀疏矩陣分解)演算法作為降維和提取特徵向量的工具,該演算法是在nmf演算法的基礎上加上顯式地稀疏因子控制而形成的一種非負矩陣分解方法。Today, oop and com technique is used widely in software area. in this paper, on the basis of nonlinear transient electromagnetic field analysis, oop technique is applied to build the basic class library of machine, and the store of large sparse matrix was combined with related algorithm closely, which save the cpu time obviously
目前面向對象編程技術( oop )和組件技術( com )已在軟體領域得到廣泛應用,本文在進行非線性瞬態電磁場理論分析的基礎上,採用oop技術,進行了電機求解的基本類庫的構建,將大型稀疏矩陣的存儲與有關演算法緊密耦合,顯著提高了運算速度,並形成了用於求解非線性瞬態電磁場的軟體包。In this thesis, we propose an efficient nmfs + rbf aggregate framework for fr, in which non - negative matrix factorization with sparseness constraints ( nmfs ) is firstly applied to learn either the holistic representations or the parts - based ones by constraining the sparseness of the basis images, and then the rbf classifier is adopted for pattern classification
本文提出了一種基於非負矩陣稀疏分解( non - negativematrixfactorizationwithsparsenessconstraints , nmfs )和rbf神經網路的人臉識別方法。通過控制稀疏度, nmfs演算法既可提取人臉全局也能提取局部特徵,再運用rbf神經網路進行模式分類。In this paper, we apply adi and high - order compact finite difference method for large - scale asymmetric sparse matrix in semiconductor device simulation
摘要採用adi與高階緊致差分相結合的方法計算大型非對稱稀疏矩陣,並實現了該演算法在半導體器件模擬中的應用。分享友人