From f7c5cbc61d5b52c749824298cfa39a95db2d879c Mon Sep 17 00:00:00 2001 From: Harpreet Date: Fri, 29 Jan 2016 16:38:03 +0530 Subject: linprog general tests added --- help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS | Bin 8587 -> 8644 bytes .../scilab_en_US_help/JavaHelpSearch/DOCS.TAB | Bin 961 -> 959 bytes .../en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS | Bin 303 -> 304 bytes .../scilab_en_US_help/JavaHelpSearch/POSITIONS | Bin 46716 -> 47502 bytes help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA | 2 +- help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP | Bin 18432 -> 18432 bytes help/en_US/scilab_en_US_help/fgoalattain.html | 94 ++++++++++----------- help/en_US/scilab_en_US_help/fminbnd.html | 12 +-- help/en_US/scilab_en_US_help/fmincon.html | 15 ++-- help/en_US/scilab_en_US_help/fminimax.html | 92 ++++++++++---------- help/en_US/scilab_en_US_help/fminunc.html | 12 +-- help/en_US/scilab_en_US_help/index.html | 6 +- help/en_US/scilab_en_US_help/jhelpset.hs | 2 +- help/en_US/scilab_en_US_help/jhelptoc.xml | 4 +- help/en_US/scilab_en_US_help/linprog.html | 27 +++--- help/en_US/scilab_en_US_help/lsqlin.html | 12 ++- help/en_US/scilab_en_US_help/lsqnonneg.html | 12 ++- help/en_US/scilab_en_US_help/qpipopt.html | 12 ++- help/en_US/scilab_en_US_help/qpipoptmat.html | 14 +-- .../section_19f4f1e5726c01d683e8b82be0a7e910.html | 8 +- .../section_508f0b211d17ea6769714cc144e6b731.html | 6 +- help/en_US/scilab_en_US_help/sym_addConstr.html | 2 +- help/en_US/scilab_en_US_help/sym_addVar.html | 2 +- help/en_US/scilab_en_US_help/sym_close.html | 2 +- .../en_US/scilab_en_US_help/sym_deleteConstrs.html | 2 +- help/en_US/scilab_en_US_help/sym_deleteVars.html | 2 +- .../scilab_en_US_help/sym_getConstrActivity.html | 2 +- .../scilab_en_US_help/sym_getConstrLower.html | 2 +- .../scilab_en_US_help/sym_getConstrRange.html | 2 +- .../scilab_en_US_help/sym_getConstrSense.html | 2 +- .../scilab_en_US_help/sym_getConstrUpper.html | 2 +- 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2 +- help/en_US/scilab_en_US_help/sym_isAbandoned.html | 2 +- help/en_US/scilab_en_US_help/sym_isBinary.html | 2 +- help/en_US/scilab_en_US_help/sym_isContinuous.html | 2 +- help/en_US/scilab_en_US_help/sym_isEnvActive.html | 2 +- help/en_US/scilab_en_US_help/sym_isInfeasible.html | 2 +- help/en_US/scilab_en_US_help/sym_isInteger.html | 2 +- .../scilab_en_US_help/sym_isIterLimitReached.html | 2 +- help/en_US/scilab_en_US_help/sym_isOptimal.html | 2 +- .../scilab_en_US_help/sym_isTargetGapAchieved.html | 2 +- .../scilab_en_US_help/sym_isTimeLimitReached.html | 2 +- help/en_US/scilab_en_US_help/sym_loadMPS.html | 2 +- help/en_US/scilab_en_US_help/sym_loadProblem.html | 2 +- .../scilab_en_US_help/sym_loadProblemBasic.html | 2 +- help/en_US/scilab_en_US_help/sym_open.html | 2 +- help/en_US/scilab_en_US_help/sym_resetParams.html | 2 +- .../scilab_en_US_help/sym_setConstrLower.html | 2 +- .../en_US/scilab_en_US_help/sym_setConstrType.html | 2 +- .../scilab_en_US_help/sym_setConstrUpper.html | 2 +- .../en_US/scilab_en_US_help/sym_setContinuous.html | 2 +- help/en_US/scilab_en_US_help/sym_setDblParam.html | 2 +- help/en_US/scilab_en_US_help/sym_setIntParam.html | 2 +- help/en_US/scilab_en_US_help/sym_setInteger.html | 2 +- help/en_US/scilab_en_US_help/sym_setObjCoeff.html | 2 +- help/en_US/scilab_en_US_help/sym_setObjSense.html | 2 +- .../scilab_en_US_help/sym_setPrimalBound.html | 2 +- help/en_US/scilab_en_US_help/sym_setStrParam.html | 2 +- help/en_US/scilab_en_US_help/sym_setVarLower.html | 2 +- help/en_US/scilab_en_US_help/sym_setVarSoln.html | 2 +- help/en_US/scilab_en_US_help/sym_setVarUpper.html | 2 +- help/en_US/scilab_en_US_help/sym_solve.html | 2 +- help/en_US/scilab_en_US_help/symphony.html | 6 +- help/en_US/scilab_en_US_help/symphonymat.html | 6 +- 81 files changed, 243 insertions(+), 215 deletions(-) (limited to 'help/en_US/scilab_en_US_help') diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS index 4ae0096..8d14e4f 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS and b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB index 5ea5806..9642809 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB and b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS index deb246c..070a255 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS and b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS index 88014cd..2019f3f 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS and b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS differ diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA index 8dc6ddf..86fa674 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA @@ -1,2 +1,2 @@ JavaSearch 1.0 -TMAP bs=2048 rt=1 fl=-1 id1=1535 id2=1 +TMAP bs=2048 rt=1 fl=-1 id1=1532 id2=1 diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP index d8580ce..d43293f 100644 Binary files a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP and b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP differ diff --git a/help/en_US/scilab_en_US_help/fgoalattain.html b/help/en_US/scilab_en_US_help/fgoalattain.html index 0f7fdc9..2981e47 100644 --- a/help/en_US/scilab_en_US_help/fgoalattain.html +++ b/help/en_US/scilab_en_US_help/fgoalattain.html @@ -12,11 +12,11 @@
- << Symphony Toolbox + << FOSSEE Optimization Toolbox - Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > fgoalattain + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > fgoalattain

fgoalattain

@@ -37,51 +37,51 @@

Calling Sequence

- 
x = fgoalattain(fun,x0,goal,weight)
-x = fgoalattain(fun,x0,goal,weight,A,b)
-x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq)
-x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub)
-x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon)
-x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon,options)
-[x,fval] = fgoalattain(...)
-[x,fval,attainfactor] = fgoalattain(...)
-[x,fval,attainfactor,exitflag] = fgoalattain(...)
-[x,fval,attainfactor,exitflag,output] = fgoalattain(...)
-[x,fval,attainfactor,exitflag,output,lambda] = fgoalattain(...)
+ 
xopt = fgoalattain(fun,x0,goal,weight)
+xopt = fgoalattain(fun,x0,goal,weight,A,b)
+xopt = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq)
+xopt = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub)
+xopt = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon)
+xopt = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon,options)
+[xopt,fval] = fgoalattain(...)
+[xopt,fval,attainfactor] = fgoalattain(...)
+[xopt,fval,attainfactor,exitflag] = fgoalattain(...)
+[xopt,fval,attainfactor,exitflag,output] = fgoalattain(...)
+[xopt,fval,attainfactor,exitflag,output,lambda] = fgoalattain(...)

Parameters

fun:

a function that accepts a vector x and returns a vector F

-
x0: -

a nx1 or 1xn matrix of double, where n is the number of variables.

-
A: -

a nil x n matrix of double, where n is the number of variables and

-
b: -

a nil x 1 matrix of double, where nil is the number of linear

-
Aeq: -

a nel x n matrix of double, where n is the number of variables

-
beq: -

a nel x 1 matrix of double, where nel is the number of linear

-
lb: -

a nx1 or 1xn matrix of double, where n is the number of variables.

-
ub: -

a nx1 or 1xn matrix of double, where n is the number of variables.

+
x0 : +

a vector of double, contains initial guess of variables.

+
A : +

a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.

+
b : +

a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.

+
Aeq : +

a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.

+
beq : +

a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.

+
lb : +

a vector of double, contains lower bounds of the variables.

+
ub : +

a vector of double, contains upper bounds of the variables.

nonlcon:

a function, the nonlinear constraints

options :

a list, containing the option for user to specify. See below for details.

-
x: -

a nx1 matrix of double, the computed solution of the optimization problem

-
fval: -

a vector of double, the value of functions at x

+
xopt : +

a vector of double, the computed solution of the optimization problem.

+
fopt : +

a double, the value of the function at x.

attainfactor:

The amount of over- or underachievement of the goals,γ at the solution.

-
exitflag: -

a 1x1 matrix of floating point integers, the exit status

-
output: -

a struct, the details of the optimization process

-
lambda: -

a struct, the Lagrange multipliers at optimum

+
exitflag : +

The exit status. See below for details.

+
output : +

The structure consist of statistics about the optimization. See below for details.

+
lambda : +

The structure consist of the Lagrange multipliers at the solution of problem. See below for details.

Description

fgoalattain solves the goal attainment problem, which is one formulation for minimizing a multiobjective optimization problem. @@ -102,14 +102,14 @@ It should be defined as type "list" and contains the following field

  • GradObj : a function, representing the gradient function of the Objective in Vector Form.
  • GradCon : a function, representing the gradient of the Non-Linear Constraints (both Equality and Inequality) of the problem. It is declared in such a way that gradient of non-linear inequality constraints are defined first as a separate Matrix (cg of size m2 X n or as an empty), followed by gradient of non-linear equality constraints as a separate Matrix (ceqg of size m2 X n or as an empty) where m2 & m3 are number of non-linear inequality and equality constraints respectively.
  • Default Values : options = list("MaxIter", [3000], "CpuTime", [600]);

    • -

    By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of minmaxObjfun. In case the GradObj option is off and GradConstr option is on, fminimax approximates minmaxObjfun gradient using numderivative toolbox.

    +

    By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of gattainObjfun. In case the GradObj option is off and GradConstr option is on, fminimax approximates gattainObjfun gradient using numderivative toolbox.

    If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.

    Furthermore, we must enable the "GradObj" option with the statement : 

    minimaxOptions = list("GradObj",fGrad);
    This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function. Note that, fGrad should be mentioned in the form of N x n where n is the number of variables, N is the number of functions in objective function.

    The constraint function must have header : 

    [c, ceq] = confun(x)
    -where x is a n x 1 matrix of dominmaxUbles, c is a 1 x nni matrix of doubles and ceq is a 1 x nne matrix of doubles (nni : number of nonlinear inequality constraints, nne : number of nonlinear equality constraints). +where x is a n x 1 matrix of doubles, c is a 1 x nni matrix of doubles and ceq is a 1 x nne matrix of doubles (nni : number of nonlinear inequality constraints, nne : number of nonlinear equality constraints). On input, the variable x contains the current point and, on output, the variable c must contain the nonlinear inequality constraints and ceq must contain the nonlinear equality constraints.

    By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of confun. In case the GradObj option is on and GradCons option is off, fminimax approximates confun gradient using numderivative toolbox.

    If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.

    @@ -145,7 +145,7 @@ It has type "struct" and contains the following fields.

    Examples

    - 
    function f1=fun(x)
+   
    function f1=gattainObjfun(x)
 f1(1)=2*x(1)*x(1)+x(2)*x(2)-48*x(1)-40*x(2)+304
 f1(2)=-x(1)*x(1)-3*x(2)*x(2)
 f1(3)=x(1)+3*x(2)-18
@@ -153,14 +153,12 @@ It has type "struct" and contains the following fields.
 f1(5)=x(1)+x(2)-8
 endfunction
 x0=[-1,1];
-
 goal=[-5,-3,-2,-1,-4];
 weight=abs(goal)
-//xopt  = [-0.0000011 -63.999998 -2.0000002 -8 3.485D-08]
-//fval  = [4 3.99]
-
+//gval  =[- 0.0000011 -63.999998 -2.0000002 -8 3.485D-08]
+//z  = [4 3.99]
 //Run fgoalattain
-[xopt,fval,attainfactor,exitflag,output,lambda]=fgoalattain(fun,x0,goal,weight)
    
+[x,fval,attainfactor,exitflag,output,lambda]=fgoalattain(gattainObjfun,x0,goal,weight)

    Authors

    • Prajwala TM, Sheetal Shalini , 2015
    @@ -171,11 +169,11 @@ It has type "struct" and contains the following fields.
    Report an issue
    - << Symphony Toolbox + << FOSSEE Optimization Toolbox - Symphony Toolbox + FOSSEE Optimization Toolbox diff --git a/help/en_US/scilab_en_US_help/fminbnd.html b/help/en_US/scilab_en_US_help/fminbnd.html index 9b64d03..46755f8 100644 --- a/help/en_US/scilab_en_US_help/fminbnd.html +++ b/help/en_US/scilab_en_US_help/fminbnd.html @@ -16,7 +16,7 @@ - Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > fminbnd + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > fminbnd

    fminbnd

    @@ -114,7 +114,8 @@ It has type "struct" and contains the following fields. //Options options=list("MaxIter",[1500],"CpuTime", [100],"TolX",[1e-6]) //Calling Ipopt -[x,fval] =fminbnd(f, x1, x2, options)
    +[x,fval] =fminbnd(f, x1, x2, options) +// Press ENTER to continue

    Examples

    //Find x in R such that it minimizes:
@@ -128,7 +129,8 @@ It has type "struct" and contains the following fields.
 x1 = [0];
 x2 = [1000];
 //Calling Ipopt
-[x,fval,exitflag,output,lambda] =fminbnd(f, x1, x2)
    +[x,fval,exitflag,output,lambda] =fminbnd(f, x1, x2) +// Press ENTER to continue

    Examples

    //The below problem is an unbounded problem:
@@ -160,7 +162,7 @@ It has type "struct" and contains the following fields.
    		- Symphony Toolbox + FOSSEE Optimization Toolbox diff --git a/help/en_US/scilab_en_US_help/fmincon.html b/help/en_US/scilab_en_US_help/fmincon.html index b2b5ca4..ea3077f 100644 --- a/help/en_US/scilab_en_US_help/fmincon.html +++ b/help/en_US/scilab_en_US_help/fmincon.html @@ -16,7 +16,7 @@ - Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > fmincon + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > fmincon

    fmincon

    @@ -159,7 +159,8 @@ It has type "struct" and contains the following fields. //Options options=list("GradObj", fGrad, "Hessian", lHess); //Calling Ipopt -[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options)
    +[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options) +// Press ENTER to continue

    Examples

    //Find x in R^3 such that it minimizes:
@@ -200,7 +201,8 @@ It has type "struct" and contains the following fields.
 //Options
 options=list("MaxIter", [1500], "CpuTime", [500], "GradObj", fGrad, "Hessian", lHess,"GradCon", cGrad);
 //Calling Ipopt
-[x,fval,exitflag,output] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options)
    +[x,fval,exitflag,output] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options) +// Press ENTER to continue

    Examples

    //The below problem is an unbounded problem:
@@ -225,7 +227,8 @@ It has type "struct" and contains the following fields.
 //Options
 options=list("MaxIter", [1500], "CpuTime", [500]);
 //Calling Ipopt
-[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,[],options)
    +[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,[],options) +// Press ENTER to continue

    Examples

    //The below problem is an infeasible problem:
@@ -288,7 +291,7 @@ It has type "struct" and contains the following fields.
    		- Symphony Toolbox + FOSSEE Optimization Toolbox diff --git a/help/en_US/scilab_en_US_help/fminimax.html b/help/en_US/scilab_en_US_help/fminimax.html index 2d086ff..a701aa7 100644 --- a/help/en_US/scilab_en_US_help/fminimax.html +++ b/help/en_US/scilab_en_US_help/fminimax.html @@ -16,7 +16,7 @@ - Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > fminimax + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > fminimax

    fminimax

    @@ -37,51 +37,49 @@

    Calling Sequence

    - 
    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(.....)

    Parameters

    fun:

    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.

    -
    x0: -

    a nx1 or 1xn matrix of doubles, where n is the number of variables, the initial guess for the optimization algorithm

    -
    A: -

    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==[]))

    -
    b: -

    a nil x 1 matrix of doubles, where nil is the number of linear inequalities

    -
    Aeq: -

    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==[]))

    -
    beq: -

    a nel x 1 matrix of doubles, where nel is the number of linear equalities

    -
    lb: -

    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

    -
    ub: -

    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

    +
    x0 : +

    a vector of double, contains initial guess of variables.

    +
    A : +

    a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.

    +
    b : +

    a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.

    +
    Aeq : +

    a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.

    +
    beq : +

    a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.

    +
    lb : +

    a vector of double, contains lower bounds of the variables.

    +
    ub : +

    a vector of double, contains upper bounds of the variables.

    nonlinfun: -

    function that computes the nonlinear inequality constraints c(x) <= 0 and nonlinear equality constraints ceq(x) = 0.

    -
    x: -

    a nx1 matrix of doubles, the computed solution of the optimization problem

    -
    fval: -

    a vector of doubles, the value of fun at x

    +

    function that computes the nonlinear inequality constraints c⋅x ≤ 0 and nonlinear equality constraints c⋅x = 0.

    +
    xopt : +

    a vector of double, the computed solution of the optimization problem.

    +
    fopt : +

    a double, the value of the function at x.

    maxfval:

    a 1x1 matrix of doubles, the maximum value in vector fval

    -
    exitflag: -

    a 1x1 matrix of floating point integers, the exit status

    -
    output: -

    a struct, the details of the optimization process

    -
    lambda: -

    a struct, the Lagrange multipliers at optimum

    -
    options: -

    a list, containing the option for user to specify. See below for details.

    +
    exitflag : +

    The exit status. See below for details.

    +
    output : +

    The structure consist of statistics about the optimization. See below for details.

    +
    lambda : +

    The structure consist of the Lagrange multipliers at the solution of problem. See below for details.

    Description

    fminimax minimizes the worst-case (largest) value of a set of multivariable functions, starting at an initial estimate. This is generally referred to as the minimax problem.

    @@ -157,11 +155,11 @@ It has type "struct" and contains the following fields. // 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

    Examples

    @@ -206,11 +204,11 @@ It has type "struct" and contains the following fields. // 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)

    Authors

    @@ -225,7 +223,7 @@ It has type "struct" and contains the following fields. - Symphony Toolbox + FOSSEE Optimization Toolbox diff --git a/help/en_US/scilab_en_US_help/fminunc.html b/help/en_US/scilab_en_US_help/fminunc.html index 7271567..636ea68 100644 --- a/help/en_US/scilab_en_US_help/fminunc.html +++ b/help/en_US/scilab_en_US_help/fminunc.html @@ -16,7 +16,7 @@ - Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > fminunc + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > fminunc

    fminunc

    @@ -114,7 +114,8 @@ It has type "struct" and contains the following fields. //Options options=list("MaxIter", [1500], "CpuTime", [500], "Gradient", fGrad, "Hessian", fHess); //Calling Ipopt -[xopt,fopt,exitflag,output,gradient,hessian]=fminunc(f,x0,options)
    +[xopt,fopt,exitflag,output,gradient,hessian]=fminunc(f,x0,options) +// Press ENTER to continue

    Examples

    //Find x in R^2 such that the below function is minimum
@@ -126,7 +127,8 @@ It has type "struct" and contains the following fields.
 //Starting point
 x0=[2,1];
 //Calling Ipopt
-[xopt,fopt]=fminunc(f,x0)
    +[xopt,fopt]=fminunc(f,x0) +// Press ENTER to continue

    Examples

    //The below problem is an unbounded problem:
@@ -164,7 +166,7 @@ It has type "struct" and contains the following fields.
    		- Symphony Toolbox + FOSSEE Optimization Toolbox diff --git a/help/en_US/scilab_en_US_help/index.html b/help/en_US/scilab_en_US_help/index.html index 07b853a..7bbe95e 100644 --- a/help/en_US/scilab_en_US_help/index.html +++ b/help/en_US/scilab_en_US_help/index.html @@ -28,10 +28,10 @@

    -

    Symphony Toolbox

    -
      +

      FOSSEE Optimization Toolbox

      +
        -
      • Symphony Toolbox +
      • FOSSEE Optimization Toolbox
        • fgoalattain — Solves a multiobjective goal attainment problem
          • diff --git a/help/en_US/scilab_en_US_help/jhelpset.hs b/help/en_US/scilab_en_US_help/jhelpset.hs index 94c2e7e..373577e 100644 --- a/help/en_US/scilab_en_US_help/jhelpset.hs +++ b/help/en_US/scilab_en_US_help/jhelpset.hs @@ -2,7 +2,7 @@ -Symphony Toolbox +FOSSEE Optimization Toolbox top diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml index 4422b0b..c4d5a12 100644 --- a/help/en_US/scilab_en_US_help/jhelptoc.xml +++ b/help/en_US/scilab_en_US_help/jhelptoc.xml @@ -1,8 +1,8 @@ - - + + diff --git a/help/en_US/scilab_en_US_help/linprog.html b/help/en_US/scilab_en_US_help/linprog.html index 260b8b3..c47e448 100644 --- a/help/en_US/scilab_en_US_help/linprog.html +++ b/help/en_US/scilab_en_US_help/linprog.html @@ -16,7 +16,7 @@
    		- Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > linprog + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > linprog

    linprog

    @@ -41,7 +41,8 @@ xopt = linprog(c,A,b,Aeq,beq) xopt = linprog(c,A,b,Aeq,beq,lb,ub) xopt = linprog(c,A,b,Aeq,beq,lb,ub,param) -[xopt, fopt, exitflag, output, lambda] = linprog(file) +xopt = linprog(file) +xopt = linprog(file,param) [xopt,fopt,exitflag,output,lambda] = linprog( ... )

    Parameters

    @@ -77,9 +78,14 @@

    Description

    OSI-CLP is used for solving the linear programming problems, OSI-CLP is a library written in C++. Search the minimum of a constrained linear programming problem specified by :

    -

    -The routine calls Clp for solving the linear programming problem, Clp is a library written in C++.

    -

    The exitflag allows to know the status of the optimization which is given back by Ipopt. +

    +

    The routine calls Clp for solving the linear programming problem, Clp is a library written in C++.

    +

    The options allows the user to set various parameters of the Optimization problem. +It should be defined as type "list" and contains the following fields. In the current version it only contains maxiter. +

    • Syntax : options= list("MaxIter", [---]);
      • +
    • MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.
      • +
    • Default Values : options = list("MaxIter", [3000]);

    +

    The exitflag allows to know the status of the optimization which is given back by CLP.

    • exitflag=0 : Optimal Solution Found
    • exitflag=1 : Primal Infeasible
    • exitflag=2 : Dual Infeasible
      • @@ -87,7 +93,6 @@ The routine calls Clp for solving the linear programming problem, Clp is a libra
    • exitflag=4 : Solution Abandoned
    • exitflag=5 : Primal objective limit reached.
    • exitflag=6 : Dual objective limit reached.

    -

    For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/

    The output data structure contains detailed informations about the optimization process. It has type "struct" and contains the following fields.

    • output.iterations: The number of iterations performed during the search
      • @@ -95,9 +100,7 @@ It has type "struct" and contains the following fields.

      The lambda data structure contains the Lagrange multipliers at the end of optimization. In the current version the values are returned only when the the solution is optimal. It has type "struct" and contains the following fields. -

      • lambda.lower: The Lagrange multipliers for the lower bound constraints.
        • -
      • lambda.upper: The Lagrange multipliers for the upper bound constraints.
        • -
      • lambda.eqlin: The Lagrange multipliers for the linear equality constraints.
        • +
        • lambda.eqlin: The Lagrange multipliers for the linear equality constraints.
        • lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.

    @@ -159,7 +162,7 @@ It has type "struct" and contains the following fields.

    Examples

    filepath = get_absolute_file_path('linprog.dem.sce');
 filepath = filepath + "exmip1.mps"
-[xopt,fopt,exitflag,output,lambda] =linprog(filepath);
    +[xopt,fopt,exitflag,output,lambda] =linprog(filepath)

    Authors

    @@ -174,7 +177,7 @@ It has type "struct" and contains the following fields. - Symphony Toolbox + FOSSEE Optimization Toolbox diff --git a/help/en_US/scilab_en_US_help/lsqlin.html b/help/en_US/scilab_en_US_help/lsqlin.html index eb1b38d..1343385 100644 --- a/help/en_US/scilab_en_US_help/lsqlin.html +++ b/help/en_US/scilab_en_US_help/lsqlin.html @@ -16,7 +16,7 @@ - Symphony Toolbox + FOSSEE Optimization Toolbox @@ -29,7 +29,7 @@ - Symphony Toolbox >> Symphony Toolbox > lsqlin + FOSSEE Optimization Toolbox >> FOSSEE Optimization Toolbox > lsqlin

    lsqlin

    @@ -82,6 +82,12 @@

    Search the minimum of a constrained linear least square problem specified by :

    The routine calls Ipopt for solving the linear least square problem, Ipopt is a library written in C++.

    +

    The options allows the user to set various parameters of the Optimization problem. +It should be defined as type "list" and contains the following fields. +

    The exitflag allows to know the status of the optimization which is given back by Ipopt.