The least absolute deviations are using widely used in engineering because of it s robustness,but the algorithm solving the least absolute deviation is not efficient.

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Then, we construct a network model of the max-min spanning arborescence problem, we discuss the essential character of the max-min spanning arborescence and deduce an effective algorithm to solve this problem based on the relationship between the minimum in-arc circuit and the max-min spanning arborescence.

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The relationship between the minimum zone circle, minimum circumscribed circle and maximum inscribed circle was studied. Based on their relationship, the minimum zone circle and minimum circumscribed circle, maximum inscribed circle were transformed mutually.

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Thus, evaluation of the minimum zone circle could be solved effectively.Three methods of assessment of roundness error were proposed in this dissertation. Based on the criteria of the minimum zone circle, minimum circumscribed circle and maximum inscribed circle and convex hull, a unified approach was proposed to evaluate roundness error of the minimum zone circle, minimum circumscribed circle and maximum inscribed circle. Evaluation of the minimum zone circle, minimum circumscribed circle and maximum inscribed circle was fulfilled with rotation based the contact pattern. The minimum circumscribed circle and maximum inscribed circle were evaluated with curvature and the curvature circle.

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The results showed that:(1) The characteristic frequency of IC neurons of house mouse was increased with increasing recording depth, and the masker intensity used was positive relevant to the IC neurons\' minimum threshold. When the minimum threshold was high, the masker intensity needed was loud, vice versa.(2) According to the masking rate at MT+10dB of IC neurons to frequency modulation and tone burst, the IC neurons were classified as three types: type I neurons ( 72/113, 63.7%), in which the masking effect of TB was better than frequency modulation ;type II neurons (32/113, 28.3%), in which the masking effects of the two maskers were same; type III neurons( 9/113, 8.0%), in which the masking effect of FM was better than TB.(3) Type I neurons, the minimum threshold of which was low, but the Q_(10) and DR values of which were large; in type III neurons, the minimum threshold was high, the Q_(10) and DR values were narrow; in type III neurons, the MT Q_(10) and DR values were in between.(4) Both FM and TB could make the MT of type I neurons shift upward distinctively, but the effect of TB was more significant than FM.(5) The Q_(10) values of type I neurons became narrower under the influence of TB, were unaffected under the influence of FM.

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This thesis introduces the basic principle of the least square data fitting.. According to the least square data fitting ,the paper studies and analyzes the linear least square fitting ,the quantic least square fitting and nonlinear least square fitting respectively . By using the mathimatic instrument Matlab , this paper shows a lot of examples of interrelated questions. Using Matlab makes the theory of data fitting easier to be understood.

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The solutions of ProblemⅠ,ⅡandⅢare discussed by using the generalized conjugate gradient method. When the equation is consistent, the solutions such as symmetric, skew-symmetric, centrosymmetric, centroskew symmetric, reflexive, antireflexive, bisymmetric or symmetric and antipersymmetric are successfully found; When the equation is inconsistent, the least-squares solutions such as symmetric, skew-symmetric, centrosymmetric, centroskew symmetric, reflexive, antireflexive, bisymmetric or symmetric and antipersymmetric are also found successfully. The generalized conjugate gradient method has the following traits:(1) It can judge automatically the information of solutions.

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At each level, the parameters, hyper parameters and model parameters are adjusted to minimize the Kullback-Leibler divergence between their posteriors and real distributions so that the variable free energy can approach the real free energy.

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least dement：最小元,最小元素

least common multiple | 最小公倍數(shù),最小公倍式 | least dement | 最小元,最小元素 | least energy principle | 最小能量原理

minimax estimate for binomial distribution：二項分布的最小最大估計

minimax error norm 極大極小誤差范數(shù) | minimax estimate for binomial distribution 二項分布的最小最大估計 | minimax estimate of location parameter 局部參數(shù)的最小最大估計,局部參數(shù)的最小最大...

least squares method：最小平方法 / 最小二乘法

Least Squared Criterion,最小二乘方準則 | Least squares method最小平方法 / 最小二乘法 | Least-absolute-residuals estimates, 最小絕對殘差估計

minimax estimate of location parameter：局部參數(shù)的最小最大估計,局部參數(shù)的最小最大估計

minimax estimate for binomial distribution 二項分布的最小最大估計 | minimax estimate of location parameter 局部參數(shù)的最小最大估計,局部參數(shù)的最小最大估計 | minimax loss 極小極大損失

minimality：最小 最小性 極小性

minimalinhibitoryconcentrationMIC 最小抑菌濃度 | minimality 最小 最小性 極小性 | minimalization 極小化 取極小值

minimize button：最小化按鈕 最小化按鈕

minimize all the windows 最小化所有窗口 所有視窗縮到最小 | minimize button 最小化按鈕 最小化按鈕 | minimize when system startup 啟動時最小化 啟動時縮到最小

minimum usable field-strength Emin, minimum usable power flux density Pmin：最小可用場強,最小可用功率通量密度

最小號長 The Minimum Number Length | 最小可用場強,最小可用功率通量密度minimum usable field-strength Emin, minimum usable power flux density Pmin | 最小碼距 minimum distance

minimum deviation：最小偏差;最小偏向

"miniature lagoon","小潟湖;假潟湖" | "minimum deviation","最小偏差;最小偏向" | "minimum ionizing speed","最小游(離)速(度)"

minimum deviation：最小偏向;最小偏差;最小偏角

minimum design weight 最小設計重量 | minimum deviation 最小偏向;最小偏差;最小偏角 | minimum drag 最小阻力

minimized window：最小化窗口 最小化視窗

minimize 最小化 縮到最小 | minimized window 最小化窗口 最小化視窗 | minimized 最小化的 最小化的