object NonlinearMinimizer extends Serializable
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- type BDV = DenseVector[Double]
- case class Projection(proximal: Proximal) extends Product with Serializable
- case class ProximalPrimal[T](primal: DiffFunction[T], u: T, z: T, rho: Double)(implicit space: MutableInnerProductModule[T, Double]) extends DiffFunction[T] with Product with Serializable
Proximal modifications to Primal algorithm for scaled ADMM formulation AdmmObj(x, u, z) = f(x) + rho/2*||x - z + u||2 dAdmmObj/dx = df/dx + rho*(x - z + u)
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- def apply(ndim: Int, constraint: Constraint, lambda: Double, usePQN: Boolean = false): FirstOrderMinimizer[BDV, DiffFunction[BDV]]
A compansion object to generate projection based minimizer that can use SPG/PQN as the solver
A compansion object to generate projection based minimizer that can use SPG/PQN as the solver
- ndim
the problem dimension
- constraint
one of the available constraint, possibilities are x>=0; lb<=x<=ub;aeq*x = beq; 1'x = s, x >= 0; ||x||1 <= s
- lambda
the regularization parameter for most of the constraints
- returns
FirstOrderMinimizer to optimize on f(x) and proximal operator
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- def project(proximal: Proximal, maxIter: Int = -1, m: Int = 10, tolerance: Double = 1e-6, usePQN: Boolean = false): FirstOrderMinimizer[BDV, DiffFunction[BDV]]
A subset of proximal operators can be represented as Projection operators and for those operators, we give an option to the user to choose a projection based algorithm.
A subset of proximal operators can be represented as Projection operators and for those operators, we give an option to the user to choose a projection based algorithm. The options available for users are SPG (Spectral Projected Gradient) and PQN (Projected Quasi Newton)
- proximal
operator that defines proximal algorithm
- returns
FirstOrderMinimizer to optimize on f(x) and proximal operator
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