trait MinimizerLP extends Error
The MinimizerLP trait sets the pattern for optimization algorithms for solving
Linear Programming (LP) problems of the form:
minimize c x subject to a x <= b, x >= 0
where a is the constraint matrix b is the limit/RHS vector c is the cost vector
Classes mixing in this trait must implement an objective function 'objF' an iterative method (solve) that searches for improved solutions 'x'-vectors with lower objective function values.
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Abstract Value Members
- abstract def objF(x: VectoD): Double
The objective function, e.g., c x.
The objective function, e.g., c x.
- x
the coordinate values of the current point
- abstract def solve(): VectoD
Run the simplex algorithm starting from an initial BFS and iteratively find a non-basic variable to replace a variable in the current basis so long as the objective function improves.
Run the simplex algorithm starting from an initial BFS and iteratively find a non-basic variable to replace a variable in the current basis so long as the objective function improves. Return the optimal solution vector.
Concrete Value Members
- def check(x: VectoD, y: VectoD, f: Double): Boolean
Determine whether the current solution is correct.
Determine whether the current solution is correct.
- x
the primal solution vector x
- y
the dual solution vector y
- f
the minimum value of the objective function
- final def flaw(method: String, message: String): Unit
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- Error