Convex optimization neural network

  • Is neural network convex optimization?

    Convex problems, if possible, will be one of the best alternative.
    However, convex optimizations in Neural Networks are still in development with the nature that Neural Networks is non-convex.
    CVXPY still needs to define the objective function to solve, and current cost functions in use isn't suitable for it.Jan 23, 2020.

  • What is convex optimization in machine learning?

    Convex optimization can be used to optimize algorithms by improving the speed at which they converge to a solution.
    Additionally, it can be used to solve linear systems of equations by finding the best approximation to the system, rather than computing an exact answer..

  • The process of minimizing (or maximizing) any mathematical expression is called optimization.
    Optimizers are algorithms or methods used to change the attributes of the neural network such as weights and learning rate to reduce the losses.
    Optimizers are used to solve optimization problems by minimizing the function.
  • This is because RELU (style) Activation Functions are generally some of the most common types of activation functions being used - yet the same difficulties concerning mon-convex optimization still remain.
    Thus, I would like to think that Neural Networks with RELU Activation Functions are still generally non-convex.
• convex optimization formulations of neural networks. • semi-infinite • the dual of the dual yields claimed convex neural network problem pconvex = min.

Can neural networks solve convex quadratic programming problems?

Huang X, Cui B (2016) A novel neural network for solving convex quadratic programming problems subject to equality and inequality constraints

Neurocomputing 214:23–31 Kanzow C, Yamashita N, Fukushima M (1997) New NCP-functions and their properties

What is convex programming?

Convex programming is a widespread class of NLP problems where the objective function and constraints are convex functions

NNs are computing systems composed of a number of highly interconnected simple information processing units, and thus can usually solve optimization problems faster than most popular optimization algorithms

Neural networks with linear activation functions and square loss will yield convex optimization (if my memory serves me right also for radial basis function networks with fixed variances). However neural networks are mostly used with non-linear activation functions (i.e. sigmoid), hence the optimization becomes non-convex.

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