Update, Abstract function, derivative, gradient, dot product and functional 
derivative are supported in SymJava

<https://lh4.googleusercontent.com/-joR1PLBlk4I/VMFLfS6hulI/AAAAAAAAA18/GG7grceg8HE/s1600/ex4_L.png>

<https://lh5.googleusercontent.com/-3KGeBHXR1Po/VMFLkgcVNeI/AAAAAAAAA2E/-rIbPcMD9_A/s1600/ex4_grad.png>

<https://lh6.googleusercontent.com/-2_ZSYuqVoss/VMFLmsnGSgI/AAAAAAAAA2M/_8FWp80T4gI/s1600/ex4_hessian.png>

On Thursday, January 15, 2015 at 5:38:07 PM UTC-8, yueming liu wrote:
>
> Just finished a prototype of the project SymJava ( 
> https://github.com/yuemingl/SymJava ) which is similar to sympy. 
> Actually, I borrowed some ideas from sympy. Thanks all the sympy 
> contributors.  
>
> The reason to develop this library is that sympy can NOT run on JPython 
> :(. I have another Java library FuturEye for numerical computation to solve 
> PDE constrained inverse problems. Symbolic computation is important for 
> this library since it will reduce a huge amount of time with automatic 
> functional derivative computation.
>
> Here is a piece of example code of SymJava:
>
> Expr expr = x + y * z;
>
> System.out.println(expr); //x + y*z
>
> Expr expr2 = expr.subs(x, y*y);
>
> System.out.println(expr2); //y^2 + y*z
>
> System.out.println(expr2.diff(y)); //2*y + z
>
> Func f = new Func("f1", expr2.diff(y));
>
> System.out.println(f); //f1(y,z)
>
> BytecodeFunc func = f.toBytecodeFunc(); //Similar to the lambdify in sympy
>
> System.out.println(func.apply(1,2)); //4.0
>
> There are two important features:
>
> 1. Operator Overloading is implemented by using 
> https://github.com/amelentev/java-oo
>
> 2. Lambdify in sympy is implemented in SymJava by using BCEL library
>
> Let me know if any one interested in this? Just send me an email: 
> nkliuyuemingATgmail.com
>
>
>

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