Class QPSimpleMinimizer
- java.lang.Object
-
- dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
-
public class QPSimpleMinimizer extends Object
These are the utility functions to solve simple quadratic programming problems that admit analytical solutions.
-
-
Constructor Summary
Constructors Constructor Description QPSimpleMinimizer()
-
Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method Description static QPSolution
solve(QuadraticFunction f)
Solves an unconstrained quadratic programming problem of this form.static QPSolution
solve(QuadraticFunction f, double epsilon)
Solves an unconstrained quadratic programming problem of this form.static QPSolution
solve(QuadraticFunction f, LinearEqualityConstraints equal)
Solves a quadratic programming problem subject to equality constraints.static QPSolution
solve(QuadraticFunction f, LinearEqualityConstraints equal, double epsilon)
Solves a quadratic programming problem subject to equality constraints.
-
-
-
Method Detail
-
solve
public static QPSolution solve(QuadraticFunction f, double epsilon) throws QPInfeasible
Solves an unconstrained quadratic programming problem of this form. \[ \min_x \left \{ \frac{1}{2} \times x'Hx + x'p \right \} \]- Parameters:
f
- the objective functionepsilon
- a precision parameter: when a number |x| ≤ ε, it is considered 0- Returns:
- a quadratic programming solution
- Throws:
QPInfeasible
- when the quadratic programming problem is infeasible
-
solve
public static QPSolution solve(QuadraticFunction f) throws QPInfeasible
Solves an unconstrained quadratic programming problem of this form. \[ \min_x \left \{ \frac{1}{2} \times x'Hx + x'p \right \} \]- Parameters:
f
- the objective function- Returns:
- a quadratic programming solution
- Throws:
QPInfeasible
- when the quadratic programming problem is infeasible
-
solve
public static QPSolution solve(QuadraticFunction f, LinearEqualityConstraints equal, double epsilon) throws QPInfeasible
Solves a quadratic programming problem subject to equality constraints. \[ \min_x \left \{ \frac{1}{2} \times x'Hx + x'p \right \}, Ax = b \]- Parameters:
f
- the objective functionequal
- the equality constraintsepsilon
- a precision parameter: when a number |x| ≤ ε, it is considered 0- Returns:
- a quadratic programming solution
- Throws:
QPInfeasible
- when the quadratic programming problem is infeasible
-
solve
public static QPSolution solve(QuadraticFunction f, LinearEqualityConstraints equal) throws QPInfeasible
Solves a quadratic programming problem subject to equality constraints. \[ \min_x \left \{ \frac{1}{2} \times x'Hx + x'p \right \}, Ax = b \]- Parameters:
f
- the objective functionequal
- the equality constraints- Returns:
- a quadratic programming solution
- Throws:
QPInfeasible
- when the quadratic programming problem is infeasible
-
-