ndarray-optimization v0.1.0

ndarray-optimization

An optimization library for ndarrays.

Available Functions

• Unconstrained Optimization
  • Parabolic Line Search
  • Steepest Descent
  • Quasi-Newton Methods
    • Rank 1 Update
    • Rank 2 DFP Update
    • Rank 2 BFGS Update
• Constrained Optimization
  • Kuhn-Tucker Conditions

Options structure

Optimization methods have very similar inputs, so the options structure provides a common interface for methods.

options = {
  'objective': {
    'start': <ndarray>,
    'func': <function>,
    'gradient': {
      'func': <string> or <function>,
      'delta': <number>
    }
  },
  'update': {
    'hessianInverse': <boolean>,
    'type': <string>
  },
  'constraints': {
    'equality': [
      {
        'func': <function>,
        'gradient': {
          'func': <string> or <function>,
          'delta': <number>
        }
      },
      {
        'func': <function>,
        'gradient': {
          'func': <string> or <function>,
          'delta': <number>
        }
      },
      ...
    ],
    'inequality': [
      {
        'func': <function>,
        'gradient': {
          'func': <string> or <function>,
          'delta': <number>
        }
      },
      {
        'func': <function>,
        'gradient': {
          'func': <string> or <function>,
          'delta': <number>
        }
      },
      ...
    ]
  },
  'solution': {
    'tolerance': <number>,
    'maxIterations': <number/integer>
  }
}

Modules

objective

Required for all routines. This defines the objective function that is to be minimized.

• "start": the n-dimensional start position for the optimization routine.
• "func": this is the scalar objective function.
  • Options:
    • function(X) { ... }: a function that takes a n-dimensional vector X as its only argument and returns a scalar.
• "gradient": this is the gradient of the objective function is determined.
  • "func": this takes the current n-dimensional position vector and evaluates the gradient at that position.
    • Options:
      • function(X, grad) { ... }: a function that evaluates at X and modifies the grad argument.
      • "forwardDifference": the string literal that specifies use of the objective function to calculate the derivative numerically using the forward difference method.
      • "backwardDifference": the string literal that specifies use of the objective function to calculate the derivative numerically using the backward difference method.
      • "centralDifference": the string literal that specifies use of the objective function to calculate the derivative numerically using the central difference method.
  • "delta": a number that specifies the numerical step that the numerical derivatives will take. This is only used when numerical derivatives are specified.
• "solution": this defines how a solution is to be determined.
  • "tolerance": the tolerance that must be achieved in order to count as a valid solution. To count as a solution, either the objective function or the gradient norm must be below this tolerance.
  • maxIterations: the maximum number of iterations that the optimization routine will run before quitting. The solution given when this is reached is not necessarily valid.

update

Used where Hessian and Hessian inverse updates are required.

• "hessianInverse": a boolean that indicates if the inputs are supposed to update the Hessian inverse or just the regular Hessian.
• "type": the type of update to perform on the inputs.
  • Options:
    • "rank1": a rank-1 update.
    • "rank2-dfp": a rank-2 update using the DFP algorithm.
    • "rank2-bfgs": a rank-2 update using the BFGS algorithm. This is the default if the string is mangled.

constraints

Used in constrained algorithms, such as sequential quadratic programming.

• "equality": an array of objects that represent equality constraints (i.e. g(x) = 0) that must be satisfied. The constraint function is considered satisfied if the scalar function is less than the tolerance specified.
• "inequality": an array of objects that represent inequality constraints (i.e. g(x) >= 0) that must be satisfied. The constraint function is considered satisfied if the scalar function value is greater than zero.

Each constraint is an object with the following properties:

• "func": the function that defines the constraint.
• "gradient": an object that defines the gradient of the constraint (see objective gradient above).

Notes

• The convention is to define everything as a 2D array, including column vectors. The ndarray library requires different arguments for a 1D vector and a 2D matrix with 1 column when it comes to the .get() and .set() functions. Sometimes, a NaN is returned if this isn't done correctly, so 2D matrices are required.

• The rank update algorithms rely on the Hessian matrix being symmetric locally. Although this is often the case, there are cases where it is not. This is usually a problem with differentiability of the objective function. A lack of symmetry may lead to an erroneous result.

License

© 2016 Tim Bright. MIT License.

Authors

Tim Bright