Matlab优化函数中options选项的修改.doc

Matlab优化函数中options选项的修改(2010-11-12 10:43:58) 转载标签： 杂谈通常在使用Matlab优化工具箱中的函数时，需要根据不同要求修改优化选项，例如最大迭代次数、x处的终止容限等等。可通过 optimset 语句来修改优化选项参数：options=optimset(optionName,optionValue);如：options=optimset(tolx,1e-100); options=optimset(options,tolfun,1e-100);后面还可以依此法添加很多选项信息。下面举一个完整例子：求取f=4*x(1)2+5*x(1)*x(2)+2*x(2)2的最小值-function f,g=ff3(x)f=4*x(1)2+5*x(1)*x(2)+2*x(2)2;if nargut 1 g(1)=8*x(1)+5*x(2); g(2)=5*x(1)+4*x(2);end通过下面将优化选项结构options.GradObj设置为on来得到梯度值。 options=optimset(Gradobj,on); x0=1,1;x,fval,exitflag=fminunc(ff3,x0,options)-Options的参数描述（OPTIMSET）：%OPTIMSETPARAMETERSrivativeCheck- Compare user supplied derivatives (gradients orJacobian)%to finite-differencing derivatives on | off%Diagnostics - Print diagnosticinformation about the function tobe%minimized or solved on | off%DiffMaxChange - Maximum change invariables for finite differencegradients% positive scalar| 1e-1 %DiffMinChange - Minimum change invariables for finite differencegradients% positive scalar| 1e-8 %Display - Level of display off | iter| notify | final %GoalsExactAchieve - Number of goals to achieve exactly (do not over- or% under-achieve) positive scalar integer | 0%GradConstr - Gradients for the nonlinear constraints defined by user% on | off %GradObj - Gradient(s) for the objective function(s) defined by user% on | off%Hessian - Hessian for the objective function defined by user on | off %HessMult - Hessian multiply function defined by user% function | %HessPattern - Sparsity pattern of the Hessian for finite-differencing% sparse matrix %HessUpdate - Quasi-Newton updating scheme% bfgs | dfp | gillmurray | steepdesc %Jacobian - Jacobian for the objective function defined by user% on | off%JacobMult - Jacobian multiply function defined by user% function | %JacobPattern - Sparsity pattern of the Jacobian for finite-differencing% sparse matrix %LargeScale - Use large-scale algorithm if possible on | off %LevenbergMarquardt - Chooses Levenberg-Marquardt over Gauss-Newton algorithm% on | off%LineSearchType - Line search algorithm choice cubicpoly | quadcubic %MaxFunEvals - Maximum number of function evaluations allowed% positive integer %MaxIter - Maximum number of iterations allowed positive integer %MaxPCGIter - Maximum number of PCG iterations allowed positive integer%MeritFunction - Use goal attainment/minimax merit function% multiobj | singleobj %MinAbsMax - Number of F(x) to minimize the worst case absolute values% positive scalar integer | 0 %PrecondBandWidth - Upper bandwidth of preconditioner for PCG% positive integer | Inf | 0 %TolCon - Termination tolerance on the constraint violation positive scalar %TolFun - Termination tolerance on the function value positive scalar %TolPCG - Termination tolerance on the PCG iteration% positive scalar | 0.1 %TolX - Termination tolerance on X positive scalar %TypicalX - Typical X values vector -注：优化问题求解时常常需要对相对误差,使用算法等进行设置.Matlab提供了options向量来对优化函数进行参数设置,options向来由18个元素.目前已经被optimset和optimget代替,详情可查阅函数optimset和optimget下面逐个说明各个参数的功能和取值.Options(1)=0(默认值)功能:控制显示,优化过程中控制输出信息,0表示不显示;1表示显示;-1表隐藏信息.Options(2)=1e-4功能:控制x的精度,自变量x的最低精度终止条件.当所有终止条件都满足的时候,优化终止.Options(3)=1e-4功能:控制 f 精度,目标函数f的最低精度终止条件.当所有终止条件都满足的时候,优化终止.options(4)=1e-7功能:约束g的最低精度终止判别条件.当所有的终止条件都满足的时候,优化终止.options(5)=0功能:选择主要优化算法.options(6)=0功能:SD算法控制.选择搜索方向算法.options(7)=0功能:搜索算法控制.选择线性搜索算法.options(8)=N/A功能:函数值,算法结束时极值点的函数值,attgoal和minimax而言,它包含一个到达因子.options(9)=0功能:梯度检查控制.当值为1时,在最初的几个迭代周期,梯度将与有限差分计算的结果比较,此时,梯度函数必须存在.options(10)=N/A功能:函数计算计数.options(11)=N/A功能:梯度计算计数.options(12)=N/A功能:限定计数,限定函数梯度计算或差分梯度计算的次数.options(13)=0功能:等式约束个数,等式约束必须放在g的前几个元素中.options(14)=0*n功能:最大迭代次数,该值缺省时被置为n的100倍,n为自变量x的个数,在fmins中,缺省为n的200倍,在fminu中,为500n.options(15)=