Questions tagged [optimization]

For questions about implementing and improving optimization algorithms used in creating AI programs, or optimization in general.

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11 views

How to determine the most appropriate metaheuristic for a problem?

I have a scheduling problem. but I don't know how to determine its complexity (NP-hard, NP-complete)? How could we justify the choice of a metaheuristic instead of other for a scheduling problem i.e. ...
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1answer
76 views

When training a CNN, what are the hyperparameters to tune first?

I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read ...
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39 views

Measuring novel configuration of points

I am trying to implement Novelty search; I understand why it can work better than the standard Genetic Algorithm based solution which just rewards according to the objective. I am working on a problem ...
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22 views

How are the lower and upper bound values of the moths determined in the Moth-Flame Optimization algorithm?

I am currently implementing the Moth-Flame Optimization (MFO) Algorithm, based on the paper: Moth-Flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm. To calculate the values of ...
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Root finding in Deep Equilibrium Models

In the Deep Equilibrium Model the neural network can be seen as "infinitely deep". Training learns a nonlinear function as usual. But there is no forward propagation of input data through ...
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25 views

Why scaling down the parameter many times during training will help the learning speed be the same for all weights in Progressive GAN?

The title is one of the special things in Progressive GAN, a paper of the NVIDIA team. By using this method, they introduced that Our approach ensures that the dynamic range, and thus the learning ...
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1answer
66 views

What is the difference between the study of evolutionary algorithms and optimization?

I have a course named "Evolutionary Algorithms", but our teacher is always mentioning the word "optimization" in his lectures. I am confused. Is he actually teaching optimization? If yes, why is the ...
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28 views

Why does Simulated Annealing not take worse solution if the Energy Difference becomes higher?

In Simualated Annealing a worse solution is accepted with this probability: $p=e^{-\frac{E(y)-E(x)}{kT}}$. If that understanding is correct: Why is this probability function used? Because it means ...
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1answer
33 views

Why is the learning rate generally beneath 1?

In all examples I've ever seen the learning rate of an optimisation method is always < 1. However, I've never found an explanation as to why this is. In addition to that, there are some cases where ...
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3answers
99 views

How to improve neural network training against a large data set of points with varying magnitude

I am currently using TensorFlow and have simply been trying to train a neural network directly against a large continuous data set, e.g. $y = [0.014, 1.545, 10.232, 0.948, ...]$ corresponding to ...
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275 views

In deep learning, is it possible to use discontinuous activation functions?

In deep learning, is it possible to use discontinuous activation functions (e.g. one with jump discontinuity)? (My guess : for example, ReLU is non-differentiable at a single point, but it still has ...
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92 views

The math behind PCA

I am trying to understand the math behind PCA. I can only solve it in the case of mapping vectors to 1 Dimensional space. How to solve the math in the case we reduce the number of dimension is greater ...
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Partial pruning in counterfactual regret minimization (CFR)

I'm using CFR to solve a large imperfect-information game. One important technique for optimizing performance of this algorithm is "partial pruning", which allows the algorithm to skip ...
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Why number of hidden units in a layer are suggested to be in powers of 2?

It is suggested that the number of hidden units in a layer should be in powers of 2 because it helps converge faster. Is it a fact and if it is, how this helps the NN learn faster. Does it have to do ...
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1answer
127 views

Which deep reinforcement learning algorithm is appropriate for my problem?

My task is to solve an optimization problem with deep reinforcement learning. I read about several algorithms like DQN, PPO, DDPG, and A2C/A3C but use cases always seem to be problems like video games ...
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40 views

How to use MOPSO to align characters vertically?

I need to efficiently align characters vertically using Multi Objective PSO. Alignment is achieved by adding spaces in between a given set of characters. ...
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1answer
774 views

What are the advantages and disadvantages of using LISP for constraint satisfaction in 3D space

We are currently working on developing a 3D modeling software that allows designers to set spatial constraints to models. The computer then should generate a 3D mesh conforming to these constraints. ...
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18 views

Which method of tree searching should be used for this board game?

Suppose the following properties of a board game: High branching factor in the beginning of the game (~500) which slowly tends towards 0 at the end of the game Evaluation of the any given board ...
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26 views

Binary data clustering by Matrix factorization

I have read an article talking about binary clustering using Matrix factorization(see attached), but i would like to understand some optimization concepts: Is it reasonable to use a Frobenius norm in ...
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1answer
48 views

Which one is more important in case of different loss optimization algorithms, Speed or the Route?

We have different kinds of algorithms to optimize the loss like AdaGrad, SGD + Momentum, etc. Some are more commonly used than the others. In some algorithms, they usually range out before they ...
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1answer
45 views

How could logistic loss be used as loss function for an ANN?

Normally, in practice, people use those loss functions with minima, e.g. $L_1$ mean absolute loss, $L_2$ mean squared error, etc. All those come with a minimum to optimize to. However, there's ...
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22 views

Finding the energy function given update rule of a single layer non-linear neural network

Consider the network with N neurons, each of which takes a $2 \times k$ input specified by the tuple $(\vec c_t, \vec \theta_t)$ to produce output $\vec{R}_t$ through an update rule on the pairwise ...
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1answer
73 views

Does this $\max$ mean that we need to maximize the regret in this regret formula?

I found that the regret in Online Machine Learning is stated as: $$\operatorname{Regret}_{T}(h)=\sum_{t=1}^{T} l\left(p_{t}, y_{t}\right)-\sum_{t=1}^{T} l\left(h(x), y_{t}\right),$$ where $p_t$ is the ...
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102 views

why the sigmoid function will be 1 and 0 if we use a fully connected layer that produce a big enough positive(res negative )output

I am using a fully connected neural network that uses sigmoid activation function. If we feed a big enough weights the sigmoid function will finally become 1 or 0, is there any solution to avoid this? ...
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1answer
52 views

How to properly optimize shared network between actor and critic?

I'm building an actor-critic reinforcment learning algorithm to solve environments. I want to use a single encoder to find representation of my environment. When I share the encoder with the actor ...
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1answer
52 views

Continuous state and continuous action Markov decision process time complexity estimate: backward induction VS policy gradient method (RL)

Model Description: Model based(assume known of the entire model) Markov decision process. Time($t$): Finite horizon discrete time with discounting factor State($x_t$): Continuous multi-dimensional ...
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1answer
60 views

What is the difference between simulated annealing and deterministic annealing?

Not sure if this is the right place, but I was wondering if someone could briefly explain to me the differences & similarities between simulated annealing and deterministic annealing? I know that ...
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1answer
65 views

How can I model this problem of delivering assets by choosing a route with reinforcement learning?

I would like to build a model based on reinforcement learning (RL) for the following scenario Recommend the best route (of cities listed for a given country) that satisfies the required criteria (...
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1answer
38 views

How to optimize neural network parameters with REINFORCE

I've seen a few mentions in papers that neural network parameters can be found using REINFORCE algorithm. It was mentioned in the context of nondifferentiable operations involving e.g. step function ...
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1answer
52 views

If the normal equation works, why do we need gradient descent?

Recently, I followed the open course CS229, http://cs229.stanford.edu/notes/cs229-notes1.pdf This lecturer introduces an alternative approach to gradient descent that is called "Normal Equation&...
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1answer
65 views

Which algorithm can I use to solve a problem with multiple objectives and constraints?

Consider a problem with many objectives. In my case, these are school grades for different courses (or subjects). To be more concrete, suppose that my current grade for the math course is $12/20$ and ...
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1answer
44 views

Can a machine learning approach solve this constrained optimisation problem?

I had done with different classification, regression and clustering approaches for predictions of values, etc. I was wondering if there is a machine learning approach for distribution of a whole based ...
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2k views

How can we use linear programming to solve an MDP?

Apparently, we can solve an MDP (that is, we can find the optimal policy for a given MDP) using a linear programming formulation. What's the basic idea behind this approach? I think you should start ...
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35 views

How many training runs are needed to obtain a credible value for performance?

I'm trying to optimize a neural network. For that, I'm changing parameters like the batch size, learning rate, weight initialization, etc. A neural network is not a deterministic algorithm, so, in ...
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60 views

How does SGD escape local minima?

SGD is able to jump out of local minima that would otherwise trap BGD I don't really understand the above statement. Could someone please provide a mathematical explanation for why SGD (Stochastic ...
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1answer
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AI that maximizes the storage of rectangular parallelepipeds in a bigger parallelepiped

As you can see in the title, I'm trying to program an AI in Java that would help someone optimize his storage. The user has to enter the size of his storage space (a box, a room, a warehouse, etc...) ...
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43 views

Simplifying Log Loss

I am reading through a paper (https://www.mitpressjournals.org/doi/pdf/10.1162/0891201053630273) where they describe logloss as a ranking function and can be simplified to the margin of the training ...
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What are swarm optimization techniques used for: training the ANN by weight optimization or for feature selection?

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. SI-based algorithms, ...
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Which of these two strategies is the best to select solutions in simulated annealing?

I am using simulated annealing (SA) for an NP-hard combinatorial optimisation problem. 1) I am testing over a range of problem instances in which the objective values can be in the 100's or in the ...
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3answers
115 views

Can we optimize an optimization algorithm?

In this answer to the question Is an optimization algorithm equivalent to a neural network?, the author stated that, in theory, there is some recurrent neural network that implements a given ...
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779 views

What are the properties of hill climbing?

What are the properties of hill climbing? More precisely, does it encounter any of the following phenomena? Local maxima Inadmissible heuristic Ridges Plateau
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1answer
69 views

What effect does batch norm have on the gradient?

Batch norm is a technique where they essentially standardize the activations at each layer, before passing it on to the next layer. Naturally, this will affect the gradient through the network. I have ...
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53 views

Can neural networks handle redundant inputs?

I have a fully connected neural network with the following number of neurons in each layer [4, 20, 20, 20, ..., 1]. I am using TensorFlow and the 4 real-valued ...
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1answer
35 views

What is the role of convex optimisation in AI systems?

Convex optimisation is defined as: I have seen a lot of talk about convex loss functions in Neural Networks and how we are optimising rewards or penalty in AI/ML systems. But I have never seen any ...
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2answers
51 views

How can I train a neural network if I don't have enough data?

I have created a neural network that is able to recognize images with the numbers 1-5. The issue is that I have a database of 16x5 images which ,unfortunately, is not proving enough as the neural ...
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1answer
83 views

Why does variational auto-encoder use the reconstruction loss?

VAE is trained to reduce the following two losses. KL divergence between inferred latent distribution and Gaussian. the reconstruction loss I understand that the first one regularizes VAE to get ...
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278 views

Why does a one-layer hidden network get more robust to poor initialization with growing number of hidden neurons?

In a nutshell: I want to understand why a one hidden layer neural network converges to a good minimum more reliably when a larger number of hidden neurons is used. Below a more detailed explanation of ...
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1answer
63 views

Oscillating around the saddle point in gradient descent?

I was reading a blog post that talked about the problem of the saddle point in training. In the post, it says if the loss function is flatter in the direction of x (local minima here) compared to ...
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3answers
1k views

What kind of optimizer is suggested to use for binary classification of similar images?

I have spent some time searching Google and wasn't able to find out what kind of optimization algorithm is best for binary classification when images are similar to one another. I'd like to read ...
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Are there optimizers that schedule their learning rate, momentum etc. autonomously?

I'm aware there are some optimizer such as Adam that adjust the learning rate for each dimension during training. However, afaik, the maximum learning rate they can have is still determined by the ...