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sparse

  • The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Proces

    The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants. The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France). The version 2.0 (February 2005) handles sparse matrices and contains an example

    標簽: discrete-time resolution functions Decision

    上傳時間: 2014-01-01

    上傳用戶:xuanjie

  • The CoinUtils project is a collection of open-source utilities developed and used by a variety of ot

    The CoinUtils project is a collection of open-source utilities developed and used by a variety of other projects in the COIN-OR repository. The project includes classes for storing and manipulating sparse matrices and vectors, performing matrix factorization, parsing input files in standard formats, building representations of mathematical programs, comparing floating point numbers with a tolerance, performing simple presolve operations, and warm starting algorithms for mathematical programs, among others.

    標簽: open-source collection CoinUtils developed

    上傳時間: 2013-12-19

    上傳用戶:xmsmh

  • The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d

    The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods. In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated

    標簽: gradient function provided versions

    上傳時間: 2016-11-07

    上傳用戶:秦莞爾w

  • Abstract - A fl exible multiscale and directional representation for images is proposed. The s

    Abstract - A fl exible multiscale and directional representation for images is proposed. The scheme combines directional fi lter banks with the Laplacian pyramid to provides a sparse representation for two- dimensional piecewise smooth signals resembling images. The underlying expansion is a frame and can be designed to be a tight frame. Pyramidal directional fi lter banks provide an effective method to implement the digital curvelet transform. The regularity issue of the iterated fi lters in the directional fi lter bank is examined.

    標簽: representation directional multiscale Abstract

    上傳時間: 2013-12-15

    上傳用戶:zxc23456789

  • Iterative Methods For sparse Linear Systems

    一本講述求解稀疏線性方程的迭代方法的外文書,閱讀需要具有較強的英語能力

    標簽: Iterative Methods Systems Linear sparse

    上傳時間: 2016-05-21

    上傳用戶:通行天下

  • A Rate Adaptive Channel Estimation Approach

    In this paper, we consider the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE).

    標簽: Estimation Millimeter Adaptive Approach Channel Systems Rate MIMO Wave for

    上傳時間: 2020-05-26

    上傳用戶:shancjb

  • 斯坦福大學-深度學習基礎教程.pdf

    斯坦福大學-深度學習基礎教程.pdfUFLDL教程 From Ufldl 說明:本教程將闡述無監督特征學習和深入學習的主要觀點。通過學習,你也將實現多個功能 學習/深度學習算法,能看到它們為你工作,并學習如何應用/適應這些想法到新問題上。 本教程假定機器學習的基本知識(特別是熟悉的監督學習,邏輯回歸,梯度下降的想法),如果 你不熟悉這些想法,我們建議你去這里 機器學習課程 (http://openclassroom.stanford.edu/MainFolder/CoursePage.php? course=MachineLearning) ,并先完成第II,III,IV章(到邏輯回歸)。 稀疏自編碼器 神經網絡 反向傳導算法 梯度檢驗與高級優化 自編碼算法與稀疏性 可視化自編碼器訓練結果 稀疏自編碼器符號一覽表 Exercise:sparse Autoencoder 矢量化編程實現 矢量化編程 邏輯回歸的向量化實現樣例 神經網絡向量化 Exercise:Vectorization

    標簽: 深度學習

    上傳時間: 2022-03-27

    上傳用戶:kingwide

  • 基于LTspice的開關電源設計及仿真

    引言開關電源(SMPS:Switch Mode Power Supply)是利用現代電力電子技術,控制開關管開通和關斷的時問比率,維持穩定輸出電壓的一種電源·非隔離式DC/DC變換具有六種基本拓撲結構:降壓(Buck)變換器升壓(Boost)變換器極性反轉升降壓(Buck2Boost)變換器Cuk(Boost2Buck 聯)變換器Sepic變換器Zeta變換器[-1,與線性電源相比,開關電源具有體積小重量輕效率高自身抗干擾性強輸出電壓范圍寬模塊化等優點。LTspice IV是LT公司推出的SPICE電路仿真軟件,具有集成電路圖捕獲和波形觀測功能。LTspice IV內置新型SPIE元件,能快速進行SMPS交互式仿真,且無元件或節點數目的限制.LTspice IV雖然與開關模式電源設計配合使用,但它并不是SMPS專用型SPICE軟件,而是一款通用型SPICE-LTspice IV內置了LT公司新型sparse矩陣求解器,采用專有的并行處理方法,實現了對任務的高效并行處理"。

    標簽: ltspice 開關電源

    上傳時間: 2022-06-26

    上傳用戶:

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