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一道英文数学题
在坐标轴上AB直线经过（3,0）（0,-1）两点
问：find the gradient of the line AB
答题形式最好用英文，越完整越好

英语翻译
Rapid and sensitive step gradient assays of glutamate,glycine,
taurine and g-aminobutyric acid by high-performance liquid
chromatography–fluorescence detection with o-phthalaldehyde–
mercaptoethanol derivatization with an emphasis on microdialysis
samples
T.Petteri Piepponena ,*,Arnis Skujinsa,b
aDepartment of Pharmacy,Division of Pharmacology and Toxicology,P.O.Box 56,University of Helsinki,FIN-00014 Helsinki,
Finland
bLaboratory of Pharmacology,Latvian Institute of Organic Synthesis,21 Aizkraukles Street,Riga LV-1006,Latvia
Received 30 November 2000; received in revised form 26 February 2001; accepted 1 March 2001
Abstract
We developed a rapid step-gradient HPLC method for determination of glutamate,glycine and taurine,and a separate
method for determination of g-aminobutyric acid (GABA) in striatal microdialysates.The amino acids were pre-column
derivatized with o-phthalaldehyde–2-mercaptoethanol by using an automated refrigerated autoinjector.Separation of the
amino acids was established with a non-porous ODS-II HPLC column,late-eluting substances were washed out with a
one-step low-pressure gradient.Concentrations of the amino acids were determined with a fixed-wavelength fluorescence
detector.The detection limit for GABA was 80 fmol in a 15 ml sample,detection limits for glutamate,glycine and taurine
were not determined because their concentrations in striatal perfusates were far above their detection limits.Total analysis
time was less than 12 min,including the wash-out step.The methods described are relatively simple,sensitive,inexpensive,
and fast enough to keep up with the microdialysis sampling.Ó 2001 Elsevier Science B.V.All rights reserved.
Keywords:Derivatization,LC; Glutamate; Glycine; Taurine; g-Aminobutyric acid

Find the gradient of the tangent to the curve y=(3x-2)(x+4) at the points:
a.(1,5) b.(0,-8) c.(-1,-15)
我是想求解答
a.16 b.10 c.4
可是我求的答案是 a.18 b.15 c.12 想知道正确答案是怎么得的
这百度知道改版了？我输入了标题按确定以为下一页会出现详细提问，结果直接就发上来了，我晕

英语翻译
ride frequencies
roll gradient
roll stiffness
还有一个句子.resulting in a stiffer vehicle in roll

请求英语专家的帮忙
Memory gradient and supermemory gradient methods are generalizations of conjugate gradient methods
,9,16,20,30].They all use not only the current iterative information but also the previous iterative informa-
on to generate a new iterate at each iteration.Moreover,limited-memory quasi-Newton method is also a
eful approach to solve large-scale minimization problems [2,3,11,12,18,25,26,31,39].It is to find a newiterate by memorizing limited gradients at each iteration.Thus,limited-memory quasi-Newton methods are
also supermemory gradient methods [19,22,27–29].
In fact,some conjugate gradient methods in minimizing non-quadratic objective functions have no global
convergence when some traditional inexact line searches are used.Many supermemory gradient methods have
no global convergence either in some cases.In order to guarantee the global convergence of supermemory gra-
dient methods and simplify the computation,some new inexact line search and curve search rules were pro-
posed [1,34–36] in practical computation.
However,there are some difficulties to overcome in analyzing supermemory gradient method with inexact
line searches.For example,the global convergence and convergence rate in general cases are interesting and
meaningful problems.How to use trust region approach to guarantee the global convergence is another chal-
lenge in algorithm design.In fact,we expect to construct such a supermemory gradient method that has three
properties:converging stably and averagely so as to solve ill-conditioned problems,simple computation so as
to solve large-scale problems,and self-adaptiveness of parameter-adjusting automatically.
In this paper we propose a new class of supermemory gradient methods for unconstrained optimization
problems.Trust region approach is used in the new algorithms to guarantee the global convergence.In each
iteration,the new algorithms generate a suitable trust region radius automatically and obtain the next iterate
by solving a simple subproblem.These algorithms converge stably and averagely due to using more iterative
information,and can be reduced to quasi-Newton methods when the iterate is close to the optimal solution.
Numerical results show that this new class of supermemory gradient methods is effective in practical
computation.The rest of this paper is organized as follows.In Section 2 we describe the algorithm and analyze some sim-
ple properties.In Sections 3 and 4 we prove its global convergence and convergence rate respectively.Numer-
ical results are reported in Section 5.Some conclusions are summarized in Section 6.（有点多,但是真的没办法啊!）受累了!

人工神经网络
TRAINLM,Epoch 0/100,MSE 72.2912/0.001,Gradient 393.63/1e-010
TRAINLM,Epoch 25/100,MSE 0.319038/0.001,Gradient 0.119081/1e-010
TRAINLM,Epoch 38/100,MSE 3.69789e-005/0.001,Gradient 0.446995/1e-010
TRAINLM,Performance goal met.
用神经网络运行完 这样的运行结果是怎么出来的呢