GENO - GENeric Optimization


GENO provides generic optimization for everyone. It is an online tool for automatically generating customized solvers for optimization problems.


Examples:

parameters
   matrix A
   vector b
variables
   vector x
min norm2(A*x-b)^2
parameters
   matrix A
   vector b
variables
   vector x
min norm2(A*x-b)^2
st x > 0
parameters
   matrix A
   vector b
variables
   vector x
min norm1(A*x-b)
parameters
   matrix X
   vector y
   scalar c
variables
   vector w
min norm1(w) + c * sum(log(exp(-y.*(X*w)) + vector(1)))
parameters
   matrix X
   vector y
   scalar c
variables
   vector w
min 0.5 * w'*w + c * sum(log(exp(-y.*(X*w)) + vector(1)))
parameters
   matrix X
   vector y
   scalar c
variables
   vector w
   scalar b
   vector xi
min 0.5*w'*w + c*sum(xi)
st -y.*(X*w + vector(b)) >= vector(1) - xi
   xi >= 0
parameters
   Matrix K symmetric
   Scalar c
   Vector y
variables
   Vector a
min
   0.5 * (a.*y)' * K * (a.*y) - sum(a)
st
   a >= 0
   y' * a == 0
parameters
   Matrix X
   Scalar a1
   Scalar a2
   Scalar n
   Vector y
variables
   Vector w
min
   n * norm2(X*w - y).^2 + a1 * norm1(w) + a2 * w' * w
parameters
   Matrix X symmetric
variables
   Matrix U
min
   norm2(X - U*U').^2
st
   U >= 0
parameters
   Matrix X
   Scalar s
   Vector y
variables
   Vector w
min
   s * norm2(y - 0.5 * tanh(0.5 * X * w) 
   + vector(0.5)).^2
parameters
   Matrix A
   Vector b
variables
   Vector x
min
   norm1(x)
st
   A*x == b
parameters
   matrix Q
   vector c
variables
   vector x
min 0.5*x'*Q*x + c'*x
st sum(x) == 1
   x >= 0

Last updated July 2019.