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I would like to minimize a function with Sage.

More precisely I would like to minimize a function of the type $$ \sum_{i=1}^{n} a_i \log \frac{1}{x_i} $$ subjected to some linear constraints.

I define a program with all the constraints. And now I would like to set up the objective function and solve.

Since that is not a linear program I wonder how to do it in Sage. Is there a way?

I would like to minimize a function with Sage.

More precisely I would like to minimize a function of the type $$ \sum_{i=1}^{n} $\sum_{i=1}^{n} a_i \log \frac{1}{x_i} $$ \frac{1}{x_i}$ subjected to some linear constraints.

I define a program with all the constraints. And now I would like to set up the objective function and solve.

Since that is not a linear program I wonder how to do it in Sage. Is there a way?

I would like to minimize a non-linear function with Sage.

More precisely I would like to minimize find the numerical minimum of a function of the type $\sum_{i=1}^{n} a_i \log \frac{1}{x_i}$ where the variables $x_i$ are subjected to some linear constraints.constraints, while $a_i$ are known coefficients.

I define Thus the constraints are essentially similar to the ones of a program with all the constraints. continuous linear program. And now I would like to set up the objective function and solve.

Since that is not a linear program I wonder how to do it in Sage. Is there a way?way? I thought that since the function is concave, a routine for convex optimization should work.

I would like to minimize a non-linear function with Sage.

More precisely I would like to find the numerical minimum of a function $\sum_{i=1}^{n} a_i \log \frac{1}{x_i}$ where the variables $x_i$ are subjected linear constraints, while $a_i$ are known coefficients.

Thus the constraints are essentially similar to the ones of a continuous linear program. And now I would like to set up the objective function and solve.

Since that this is not a linear program I wonder how to do it in Sage. Is there a way? I thought that since the function is concave, a routine for convex optimization should work.