---
name: "mlpack_optimization"
section: "3"
platform: "linux"
package: "mlpack-doc"
version: "1.0.10-1"
release: "Debian 8 (Jessie)"
platforms: ["linux"]
url: "https://new.carta.tech/mlpack_optimization"
---

# mlpack\_optimization(3)

## NAME

mlpack::optimization -

## SYNOPSIS

### Namespaces

 **test**\

### Classes

class **AugLagrangian**\
 *The* **AugLagrangian** *class implements the Augmented Lagrangian method of optimization.*  class **AugLagrangianFunction**\
 *This is a utility class used by* **AugLagrangian***, meant to wrap a LagrangianFunction into a function usable by a simple optimizer like L-BFGS.*  class **AugLagrangianTestFunction**\
 *This function is taken from 'Practical Mathematical Optimization' (Snyman), section 5.3.8 ('Application of the Augmented Lagrangian Method').*  class **ExponentialSchedule**\
 *The exponential cooling schedule cools the temperature T at every step according to the equation.*  class **GockenbachFunction**\
 *This function is taken from M.*  class **L\_BFGS**\
 *The generic L-BFGS optimizer, which uses a back-tracking line search algorithm to minimize a function.*  class **LovaszThetaSDP**\
 *This function is the Lovasz-Theta semidefinite program, as implemented in the following paper:*  class **LRSDP**\
 **LRSDP** *is the implementation of Monteiro and Burer's formulation of low-rank semidefinite programs (LR-SDP).*  class **LRSDPFunction**\
 *The objective function that* **LRSDP** *is trying to optimize.*  class **SA**\
 *Simulated Annealing is an stochastic optimization algorithm which is able to deliver near-optimal results quickly without knowing the gradient of the function being optimized.*  class **SGD**\
 *Stochastic Gradient Descent is a technique for minimizing a function which can be expressed as a sum of other functions.*

## Author

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