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Diffstat (limited to 'help/en_US/fminimax.xml')
| -rw-r--r-- | help/en_US/fminimax.xml | 86 |
1 files changed, 42 insertions, 44 deletions
diff --git a/help/en_US/fminimax.xml b/help/en_US/fminimax.xml index ddab078..efe4812 100644 --- a/help/en_US/fminimax.xml +++ b/help/en_US/fminimax.xml @@ -24,17 +24,17 @@ <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - x = fminimax(fun,x0) - x = fminimax(fun,x0,A,b) - x = fminimax(fun,x0,A,b,Aeq,beq) - x = fminimax(fun,x0,A,b,Aeq,beq,lb,ub) - x = fminimax(fun,x0,A,b,Aeq,beq,lb,ub,nonlinfun) - x = fminimax(fun,x0,A,b,Aeq,beq,lb,ub,nonlinfun,options) - [x, fval] = fmincon(.....) - [x, fval, maxfval]= fmincon(.....) - [x, fval, maxfval, exitflag]= fmincon(.....) - [x, fval, maxfval, exitflag, output]= fmincon(.....) - [x, fval, maxfval, exitflag, output, lambda]= fmincon(.....) + xopt = fminimax(fun,x0) + xopt = fminimax(fun,x0,A,b) + xopt = fminimax(fun,x0,A,b,Aeq,beq) + xopt = fminimax(fun,x0,A,b,Aeq,beq,lb,ub) + xopt = fminimax(fun,x0,A,b,Aeq,beq,lb,ub,nonlinfun) + xopt = fminimax(fun,x0,A,b,Aeq,beq,lb,ub,nonlinfun,options) + [xopt, fval] = fmincon(.....) + [xopt, fval, maxfval]= fmincon(.....) + [xopt, fval, maxfval, exitflag]= fmincon(.....) + [xopt, fval, maxfval, exitflag, output]= fmincon(.....) + [xopt, fval, maxfval, exitflag, output, lambda]= fmincon(.....) </synopsis> </refsynopsisdiv> @@ -44,36 +44,34 @@ <variablelist> <varlistentry><term>fun:</term> <listitem><para> The function to be minimized. fun is a function that accepts a vector x and returns a vector F, the objective functions evaluated at x.</para></listitem></varlistentry> - <varlistentry><term>x0:</term> - <listitem><para> a nx1 or 1xn matrix of doubles, where n is the number of variables, the initial guess for the optimization algorithm</para></listitem></varlistentry> - <varlistentry><term>A:</term> - <listitem><para> a nil x n matrix of doubles, where n is the number of variables and nil is the number of linear inequalities. If A==[] and b==[], it is assumed that there is no linear inequality constraints. If (A==[] & b<>[]), fminimax generates an error (the same happens if (A<>[] & b==[]))</para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> a nil x 1 matrix of doubles, where nil is the number of linear inequalities</para></listitem></varlistentry> - <varlistentry><term>Aeq:</term> - <listitem><para> a nel x n matrix of doubles, where n is the number of variables and nel is the number of linear equalities. If Aeq==[] and beq==[], it is assumed that there is no linear equality constraints. If (Aeq==[] & beq<>[]), fminimax generates an error (the same happens if (Aeq<>[] & beq==[]))</para></listitem></varlistentry> - <varlistentry><term>beq:</term> - <listitem><para> a nel x 1 matrix of doubles, where nel is the number of linear equalities</para></listitem></varlistentry> - <varlistentry><term>lb:</term> - <listitem><para> a nx1 or 1xn matrix of doubles, where n is the number of variables. The lower bound for x. If lb==[], then the lower bound is automatically set to -inf</para></listitem></varlistentry> - <varlistentry><term>ub:</term> - <listitem><para> a nx1 or 1xn matrix of doubles, where n is the number of variables. The upper bound for x. If ub==[], then the upper bound is automatically set to +inf</para></listitem></varlistentry> + <varlistentry><term>x0 :</term> + <listitem><para> a vector of double, contains initial guess of variables.</para></listitem></varlistentry> + <varlistentry><term>A :</term> + <listitem><para> a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.</para></listitem></varlistentry> + <varlistentry><term>b :</term> + <listitem><para> a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.</para></listitem></varlistentry> + <varlistentry><term>Aeq :</term> + <listitem><para> a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.</para></listitem></varlistentry> + <varlistentry><term>beq :</term> + <listitem><para> a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.</para></listitem></varlistentry> + <varlistentry><term>lb :</term> + <listitem><para> a vector of double, contains lower bounds of the variables.</para></listitem></varlistentry> + <varlistentry><term>ub :</term> + <listitem><para> a vector of double, contains upper bounds of the variables.</para></listitem></varlistentry> <varlistentry><term>nonlinfun:</term> - <listitem><para> function that computes the nonlinear inequality constraints c(x) <= 0 and nonlinear equality constraints ceq(x) = 0.</para></listitem></varlistentry> - <varlistentry><term>x:</term> - <listitem><para> a nx1 matrix of doubles, the computed solution of the optimization problem</para></listitem></varlistentry> - <varlistentry><term>fval:</term> - <listitem><para> a vector of doubles, the value of fun at x</para></listitem></varlistentry> + <listitem><para> function that computes the nonlinear inequality constraints c⋅x ≤ 0 and nonlinear equality constraints c⋅x = 0.</para></listitem></varlistentry> + <varlistentry><term>xopt :</term> + <listitem><para> a vector of double, the computed solution of the optimization problem.</para></listitem></varlistentry> + <varlistentry><term>fopt :</term> + <listitem><para> a double, the value of the function at x.</para></listitem></varlistentry> <varlistentry><term>maxfval:</term> <listitem><para> a 1x1 matrix of doubles, the maximum value in vector fval</para></listitem></varlistentry> - <varlistentry><term>exitflag:</term> - <listitem><para> a 1x1 matrix of floating point integers, the exit status</para></listitem></varlistentry> - <varlistentry><term>output:</term> - <listitem><para> a struct, the details of the optimization process</para></listitem></varlistentry> - <varlistentry><term>lambda:</term> - <listitem><para> a struct, the Lagrange multipliers at optimum</para></listitem></varlistentry> - <varlistentry><term>options:</term> - <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry> + <varlistentry><term>exitflag :</term> + <listitem><para> The exit status. See below for details.</para></listitem></varlistentry> + <varlistentry><term>output :</term> + <listitem><para> The structure consist of statistics about the optimization. See below for details.</para></listitem></varlistentry> + <varlistentry><term>lambda :</term> + <listitem><para> The structure consist of the Lagrange multipliers at the solution of problem. See below for details.</para></listitem></varlistentry> </variablelist> </refsection> @@ -222,11 +220,11 @@ endfunction // The initial guess x0 = [0.1,0.1]; // The expected solution : only 4 digits are guaranteed -//xopt = [4 4] -//fopt = [0 -64 -2 -8 0] +xopt = [4 4] +fopt = [0 -64 -2 -8 0] maxfopt = 0 // Run fminimax -[xopt,fopt,maxfval,exitflag,output,lambda] = fminimax(myfun, x0) +[x,fval,maxfval,exitflag,output,lambda] = fminimax(myfun, x0) // Press ENTER to continue ]]></programlisting> @@ -276,11 +274,11 @@ minimaxOptions = list("GradObj",myfungrad,"GradCon",cgrad); // The initial guess x0 = [0,10]; // The expected solution : only 4 digits are guaranteed -//xopt = [0.92791 7.93551] -//fopt = [6.73443 -189.778 6.73443 -8.86342 0.86342] +xopt = [0.92791 7.93551] +fopt = [6.73443 -189.778 6.73443 -8.86342 0.86342] maxfopt = 6.73443 // Run fminimax -[xopt,fopt,maxfval,exitflag,output] = fminimax(myfun,x0,[],[],[],[],[],[], confun, minimaxOptions) +[x,fval,maxfval,exitflag,output] = fminimax(myfun,x0,[],[],[],[],[],[], confun, minimaxOptions) ]]></programlisting> </refsection> |