Bugs fixed 4

1 files changed, 21 insertions, 21 deletions

diff --git a/help/en_US/symphonymat.xml b/help/en_US/symphonymat.xml
index 792eb15..cf95807 100644
--- a/help/en_US/symphonymat.xml
+++ b/help/en_US/symphonymat.xml
@@ -24,10 +24,10 @@
 <refsynopsisdiv>
    <title>Calling Sequence</title>
    <synopsis>
-   xopt = symphonymat(C,intcon,A,b)
-   xopt = symphonymat(C,intcon,A,b,Aeq,beq)
-   xopt = symphonymat(C,intcon,A,b,Aeq,beq,lb,ub)
-   xopt = symphonymat(C,intcon,A,b,Aeq,beq,lb,ub,options)
+   xopt = symphonymat(c,intcon,A,b)
+   xopt = symphonymat(c,intcon,A,b,Aeq,beq)
+   xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub)
+   xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub,options)
    [xopt,fopt,status,output] = symphonymat( ... )
    
    </synopsis>
@@ -36,18 +36,18 @@
 <refsection>
    <title>Parameters</title>
    <variablelist>
-   <varlistentry><term>f :</term>
+   <varlistentry><term>c :</term>
       <listitem><para> a vector of double, contains coefficients of the variables in the objective</para></listitem></varlistentry>
    <varlistentry><term>intcon :</term>
       <listitem><para> Vector of integer constraints, specified as a vector of positive integers. The values in intcon indicate the components of the decision variable x that are integer-valued. intcon has values from 1 through number of variable.</para></listitem></varlistentry>
    <varlistentry><term>A :</term>
-      <listitem><para> Linear inequality constraint matrix, specified as a matrix of double. A represents the linear coefficients in the constraints A*x ≤ b. A has size M-by-N, where M is the number of constraints and N is number of variables</para></listitem></varlistentry>
+      <listitem><para> Linear inequality constraint matrix, specified as a matrix of double. A represents the linear coefficients in the constraints A*x ≤ b. A has the size where columns equals to the number of variables.</para></listitem></varlistentry>
    <varlistentry><term>b :</term>
-      <listitem><para> Linear inequality constraint vector, specified as a vector of double. b represents the constant vector in the constraints A*x ≤ b. b has length M, where A is M-by-N</para></listitem></varlistentry>
+      <listitem><para> Linear inequality constraint vector, specified as a vector of double. b represents the constant vector in the constraints A*x ≤ b. b has size equals to the number of rows in A.</para></listitem></varlistentry>
    <varlistentry><term>Aeq :</term>
-      <listitem><para> Linear equality constraint matrix, specified as a matrix of double. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has size Meq-by-N, where Meq is the number of constraints and N is number of variables</para></listitem></varlistentry>
+      <listitem><para> Linear equality constraint matrix, specified as a matrix of double. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has the size where columns equals to the number of variables.</para></listitem></varlistentry>
    <varlistentry><term>beq :</term>
-      <listitem><para> Linear equality constraint vector, specified as a vector of double. beq represents the constant vector in the constraints Aeq*x = beq. beq has length Meq, where Aeq is Meq-by-N.</para></listitem></varlistentry>
+      <listitem><para> Linear equality constraint vector, specified as a vector of double. beq represents the constant vector in the constraints Aeq*x = beq. beq has size equals to the number of rows in Aeq.</para></listitem></varlistentry>
    <varlistentry><term>lb :</term>
       <listitem><para> Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub.</para></listitem></varlistentry>
    <varlistentry><term>ub :</term>
@@ -55,13 +55,13 @@
    <varlistentry><term>options :</term>
       <listitem><para> a list containing the the parameters to be set.</para></listitem></varlistentry>
    <varlistentry><term>xopt :</term>
-      <listitem><para> a vector of double, the computed solution of the optimization problem</para></listitem></varlistentry>
+      <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 function value at x</para></listitem></varlistentry>
    <varlistentry><term>status :</term>
-      <listitem><para> status flag from symphony.</para></listitem></varlistentry>
+      <listitem><para> status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.</para></listitem></varlistentry>
    <varlistentry><term>output :</term>
-      <listitem><para> The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration.</para></listitem></varlistentry>
+      <listitem><para> The output data structure contains detailed information about the optimization process. This version only contains number of iterations.</para></listitem></varlistentry>
    </variablelist>
 </refsection>
 
@@ -69,22 +69,22 @@
    <title>Description</title>
    <para>
 Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
-find the minimum or maximum of f(x) such that
+find the minimum or maximum of C'⋅x such that
    </para>
    <para>
 <latex>
 \begin{eqnarray}
 &amp;\mbox{min}_{x}
-&amp; C^T*x \\
-&amp; \text{subject to} &amp; A*x \leq b \\
-&amp; &amp; Aeq*x = beq \\
+&amp; C^T⋅x \\
+&amp; \text{subject to} &amp; A⋅x \leq b \\
+&amp; &amp; Aeq⋅x = beq \\
 &amp; &amp; lb \leq x \leq ub \\
 &amp; &amp; x_i \in \!\, \mathbb{Z}, i \in \!\, I
 \end{eqnarray}
 </latex>
    </para>
    <para>
-We are calling SYMPHONY written in C by gateway files for the actual computation.
+The routine calls SYMPHONY written in C by gateway files for the actual computation.
    </para>
    <para>
 </para>
@@ -94,7 +94,7 @@ We are calling SYMPHONY written in C by gateway files for the actual computation
    <title>Examples</title>
    <programlisting role="example"><![CDATA[
 // Objective function
-C = [350*5,330*3,310*4,280*6,500,450,400,100]';
+c = [350*5,330*3,310*4,280*6,500,450,400,100]';
 // Lower Bound of variable
 lb = repmat(0,1,8);
 // Upper Bound of variables
@@ -125,7 +125,7 @@ intcon = [1 2 3 4];
 // st   sum{j=1,...,n} r(i,j)x(j) <= b(i)       i=1,...,m
 //                     x(j)=0 or 1
 // The function to be maximize i.e. P(j)
-C = -1*[   504 803 667 1103 834 585 811 856 690 832 846 813 868 793 ..
+c = -1*[   504 803 667 1103 834 585 811 856 690 832 846 813 868 793 ..
 825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 ..
 877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 ..
 957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 ..
@@ -174,7 +174,7 @@ A = [   //Constraint 1
 483 336 765 637 981 980 202 35 594 689 602 76 767 693 ..
 893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ;
 ];
-nbVar = size(objCoef,1)
+nbVar = size(c,1)
 b=[11927 13727 11551 13056 13460 ];
 // Lower Bound of variables
 lb = repmat(0,1,nbVar)
@@ -194,7 +194,7 @@ xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 ..
 // Optimal value
 fopt = [ 24381 ]
 // Calling Symphony
-[x,f,status,output] = symphonymat(C,intcon,A,b,[],[],lb,ub,options);
+[x,f,status,output] = symphonymat(c,intcon,A,b,[],[],lb,ub,options);
    ]]></programlisting>
 </refsection>