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Description: Compressive sensing is the reconstruction of sparse images
or signals very few samples, by means of solving a
tractable optimization problem. In the context of MRI, this
can allow reconstruction many fewer k-space samples,
thereby reducing scanning time. Previous work has shown
that nonconvex optimization reduces still further the number
of samples required for reconstruction, while still being
tractable. In this work, we extend recent Fourier-based algorithms
for convex optimization to the nonconvex setting, and
obtain methods that combine the reconstruction abilities of
previous nonconvex approaches with the computational speed
of state-of-the-art convex methods.
-Compressive sensing is the reconstruction of sparse images
or signals very few samples, by means of solving a
tractable optimization problem. In the context of MRI, this
can allow reconstruction many fewer k-space samples,
thereby reducing scanning time. Previous work has shown
that nonconvex optimization reduces still further the number
of samples required for reconstruction, while still being
tractable. In this work, we extend recent Fourier-based algorithms
for convex optimization to the nonconvex setting, and
obtain methods that combine the reconstruction abilities of
previous nonconvex approaches with the computational speed
of state-of-the-art convex methods.
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Size: 17408 |
Author: DDDJDDKDK |
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Description: 我们设计一个新奇的规则化框架以学习相似性度量用于无约束人脸验证。我们形式化它的目标函数通过融合鲁棒性对于大规模的个人人脸的内部变化和新奇的相似性度量的辨别力。额外，我们的形式是一个凸优化问题，保证了全局最优解的存在。-we migrate such a geometric
model to address face recognition and verification together
through proposing a unified archetype hull ranking framework. Upon a scalable graph characterized by a compact
set of archetype exemplars whose convex hull encompasses
most of the training images, the proposed framework explicitly captures the relevance between any query and the
stored archetypes, yielding a rank vector over the archetype
hull. The archetype hull ranking is then d on every block of face images to generate a blockwise similarity
measure that is achieved by comparing two different rank
vectors with respect to the same archetype hull. After integrating blockwise similarity measurements with learned importance weights, we accomplish a sensible face similarity
measure which can support robust and effective face recognition and verification.
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Author: 郭继东 |
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Description: imadjust
调整图像亮度函数
J IMADJUST(I,[LOW_IN HIGH_IN],[LOW_OUT HIGH_OUT],GAMMA)
GAMMA默认值为1（线性映射），GAMMA<1——曲线上凸，GAMMA>1——曲线下凹
图像I与图像f的类型一致，任何类型的图像[LOW_IN HIGH_IN],[LOW_OUT HIGH_OUT]取值均在0—1范围内(计算时函数自己转换)-adjust image brightness function imadjust J IMADJUST (I, [LOW_IN HIGH_IN], [LOW_OUT HIGH_OUT], GAMMA) GAMMA default is 1 (linear mapping), GAMMA < 1 on convex curve, GAMMA> 1- concave curve identical to the type of image I and the image f, any type of image [LOW_IN HIGH_IN], [LOW_OUT HIGH_OUT] values are in the range of 0-1 (when calculating the function to convert their own)
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Author: 妮卡 |
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Description: 机器学习(Machine Learning, ML)是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。
它是人工智能的核心，是使计算机具有智能的根本途径，其应用遍及人工智能的各个领域，它主要使用归纳、综合而不是演绎。-Machine Learning (Machine Learning, ML) is more than one field of cross-disciplinary, involving probability theory, statistics, approximation theory, convex analysis, computational complexity theory and many other subjects. It specializes in computer simulation or how to achieve human learning behavior to acquire new knowledge or skills, reorganize existing knowledge structure so as to continuously improve their performance. It is the core of artificial intelligence, it is to make computers intelligent fundamental way across the various fields of application of artificial intelligence, it is mainly the use of induction, rather than a comprehensive interpretation.
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Author: kaikai |
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Description: 用于L1范数凸优化的一款matlab工具包，对压缩感知学者有一定帮助。-L1 norm for a convex optimization matlab toolkit, compression perception scholars have some help.
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Author: 李明 |
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Description: Matlab code for generating sparse signal and compressing and recovering the signal using convex optimization.
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Author: Tanoli Khan |
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Description: We hope that this book will be useful as the primary or alternate textbook for
several types of courses. Since 1995 we have been using drafts of this book for
graduate courses on linear, nonlinear, and convex optimization (with engineering
applications) at Stanford and UCLA. We are able to cover most of the material,
though not in detail, in a one quarter graduate course. Aone semester course allows
for a more leisurely pace, more applications, more detailed treatment of theory,
and perhaps a short student project. A two quarter sequence allows an expanded
treatment ofthe more basic topics such as linear andquadratic programming(which
are very useful for the applications oriented student), or a more substantial student
project.
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Author: yao |
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Description: 凸优化算法实现DOA估计，可以分辨多个目标，估计出目标方位，且有很高的分辨性能。-Convex optimization algorithm to achieve DOA estimation, can distinguish multiple targets, to estimate the direction of the target, and has a high resolution performance.
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Author: 赵安琦 |
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Description: 判点在凸多边形内,顶点按顺时针或逆时针给出,在多边形边上返回0-Judgment point in the convex polygon, the vertex clockwise or counterclockwise given, the edge of the polygon back to 0
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Size: 2048 |
Author: 曹城生 |
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Description: 俞立老师书本例题的仿真程序，凸优化，最优解，可行性等问题。对线性不等式的编程有很好的参考性-Yu Li, teacher simulation of the book program, convex optimization, the optimal solution, feasibility and other issues. The programming of linear inequalities has a good reference
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Size: 6144 |
Author: 郭婷婷 |
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Description: 通过绘制图像的轮廓，来识别是否存在凸包缺陷问题-By drawing the outline of the image to identify the existence of the convex hull defects
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Author: 乘凉 |
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Description: 用于混响背景语音分离的结构稀疏模型(Strutured sparisty model)方法-To further tackle the ambiguity
of the reflection ratios, we propose a novel formulation of the
reverberation model and estimate the absorption coefficients
through a convex optimization exploiting joint sparsity model
formulated upon spatio-spectral sparsity of concurrent speech
representation. The acoustic parameters are then incorporated
for separating individual speech signals through either structured
sparse recovery or inverse filtering the acoustic channels.
The experiments conducted on real data recordings of spatially
stationary sources demonstrate the effectiveness of the proposed
approach for speech separation and recognition.
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Author: bigbigtom |
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Description: 基于经典的增广拉格朗日乘子法, 对求解一类带有特定结构（主要是针对凸规划）的非光滑等式约束优化问题, 我们提出、分析并测试了一个新算法. 在极小化增广拉格朗日函数的每一步迭代中, 该算法有效结合了带有非单调线性搜索的交替方向技术, 我们建立了算法的收敛性, 并用它来求解在带有全变差正则化的图像恢复问题.-Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained nonsmooth optimization problems (chiefly but not necessarily convex programs) with
a particular structure. The algorithm effectively combines an alternating direction
technique with a nonmonotone line search to minimize the augmented Lagrangian
function at each iteration. We establish convergence for this algorithm, and apply it
to solving problems in image reconstruction with total variation regularization.
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Author: hakunalife |
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Description: ADMM方法对于求解线性约束凸优化问题是有效的。在本文中，我们提出了一个邻近线性算法，同伦算法习惯于求解每个迭代点的子问题。在合适的条件下，这个全局收敛和的收敛速度是所提出算法被证明的最糟糕的情况。一个初始的数值结果表明提出方法的有效性。-ADMM method for solving linear constrained convex optimization problem is effective. In this paper, we propose a near linear algorithm, homotopy method used to solve the problem of sub-point of each iteration. Under appropriate conditions, the global convergence and convergence rate is the worst case of the proposed algorithm is proved. The numerical results show that an initial validity of the proposed method.
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Size: 147456 |
Author: akd |
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Description: 压缩感知恢复算法，使用新的范数来提升图像恢复能力，包含论文和代码。-We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the image level curves and 2) reconstruction of an image fitting the normal vectors, the compressed sensing measurements and the sparsity constraint. The proposed technique can naturally extend to non local operators and graphs to exploit the repetitive nature of textured images in order to recover fine detail structures. In both cases, the problem is reduced to a series of convex minimization problems that can be efficiently solved with a combination of variable splitting and augmented Lagrangian methods, leading to fast and easy-to-code algorithms.
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Size: 1130496 |
Author: wf |
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Description: 程序功能：
1、对原始dicom病例进行中值滤波（滤波模板为3x3）
2、进行阈值分割及筛除步骤
3、导入肝掩膜凸壳，并对其腐蚀（腐蚀结构元素半径为10）
4、对2步骤中结果与肝掩膜做与运算- Program features: 1 of the original cases were dicom median filter (filter template for 3x3) 2, threshold segmentation and screening Step 3 import liver mask convex hull, and its corrosion (corrosion of structural elements radius 10) 4 , the result of the second step and do a mask and liver operation
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Author: sherrytuan |
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Description: This paper concerns a method for digital circuit optimization based on formulating the problem as a geometric program
(GP) or generalized geometric program (GGP), which can be transformed to a convex optimization problem and then very
efficiently solved. We start with a basic gate scaling problem, with delay modeled as a simple resistor-capacitor (RC) time
constant, and then add various layers of complexity and modeling accuracy, such as accounting for differing signal fall and
rise times, and the effects of signal transition times. We then consider more complex formulations such as robust design
over corners, multimode design, statistical design, and problems in which threshold and power supply voltage are also
variables to be chosen. Finally, we look at the detailed design of gates and interconnect wires, again using a formulation
that is compatible with GP or GGP.
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Size: 439296 |
Author: Hai Van Phu |
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Description: Boyd和Vandenberghe的经典的凸优化教材的配套课件，内容十分详细，纯英文版的上课的课件，欢迎有志者下载学习-The classic Boyd and Vandenberghe convex optimization teaching materials supporting courseware content is very detailed, pure class courseware in English, where there welcome to download learning
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Size: 1582080 |
Author: BITER |
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Description: convex optimization text book
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Size: 5622784 |
Author: hihi |
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Description: 提出了一种基于整体（凹凸）特性的脱机大写体英文字母识别方法。首先计算字母图像的赋值背景，再从中提取凹凸特性，然后根据凹凸特性构建分类表进行分类识别。-This paper presents an off-line capitalization method based on the whole (concavity) feature. First, calculate the background of the letter image, and then extract the convex and concave characteristics, and then according to the convex characteristics of the classification table for classification and identification.
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Author: 东方 |
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