Questions tagged [optimization]

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

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14 views

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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2answers
90 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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27 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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16 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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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
43 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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1answer
72 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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1answer
37 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
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
64 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
37 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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1answer
59 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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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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58 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
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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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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23 views

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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20 views

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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45 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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3answers
96 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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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
125 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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62 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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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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1answer
54 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
927 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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2answers
744 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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35 views

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 ...
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1answer
72 views

What are advantages of using meta-heuristic algorithms on optimization problems?

What are the advantages and disadvantages of using meta-heuristic algorithms on optimization problems? Simply, why do we use meta-heuristic algorithms, like PSO, over traditional mathematical ...
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2answers
236 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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2answers
98 views

Solving a planning if finding the goal state is part of the problem

I having trouble finding some starting points for solving an occupancy problem which seems like a good candidate for ai. Assume the following situation: In a company I have n cars and m employees. ...
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1answer
57 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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19 views

Optimizer effects on neural network with two outputs

I'm confused about the following issue. Let assume that we have a neural network that takes one input and two outputs. I try to visualize my model like as follows: ...
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28 views

Imposing contraints on sequence of image classifications

Are there example implementations of networks that apply constraints across sequences of image classifications where class labels are ordinal numbers? For example, to cause the output of a CNN to ...
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1answer
41 views

How should I weight the factors that affect the choice of an action in a strategy board game with multiple actions?

I have written an AI that plays a strategy board game. There are lots of different types of moves (e.g. attack, defend, help ally colony, etc.). I calculate the best moves to do depending on a ...
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33 views

Is logistic regression used for unconstrained or constrained optimisation problems?

Is logistic regression used for unconstrained or constrained optimization problems, and why?
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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
631 views

What's the difference between RMSE and Euclidean distance, and when to use a custom loss? [closed]

I'm searching for a loss function that fits my project. Actually, I have two questions, but they are in the same direction. I take a look at the definition of the root mean squared error (RMSE) and ...
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113 views

How can I assign agents to tasks based on time and affinity?

I am working on an assignment problem. Consider $K$ agents $A_1, \dots A_K$ and $N$ tasks $T_1, \dots T_N$. Each task has a certain time $t(T_i)$ to be completed and each agent has a matching (or ...
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95 views

How does the automated temperature adjustment step work in Soft Actor-Critic?

In section 5 of the paper Soft Actor-Critic Algorithms and Applications, it is proposed an optimization problem to obtain an optimal temperature parameter $\alpha^*_t$. First, one uses the original ...
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64 views

Grey Wolf Optimization - Issue with Dimension [closed]

I'm trying to use the grey wolf optimization (GWO) for texts clustering. I used this code, https://github.com/7ossam81/EvoloPy-NN/blob/master/selector.py I tried using the dimension 30 for the GWO as ...
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15 views

Choosing best combinations from all possible combination expressions based few variables, unary operators, binary operators

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...
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1answer
61 views

What are examples of optimization problems that can be solved using a genetic algorithm?

I'm trying to learn how a genetic algorithm can solve optimization problems. I have already learned how a genetic algorithm can solve knapsack, TSP and set cover problems. I'm looking for some other ...
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1answer
630 views

What are the implications of the “No Free Lunch” theorem for machine learning?

The No Free Lunch (NFL) theorem states (see the paper Coevolutionary Free Lunches by David H. Wolpert and William G. Macready) any two algorithms are equivalent when their performance is averaged ...
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44 views

How can we conclude that an optimization algorithm is better than another one

When we test a new optimization algorithm, what the process that we need to do?For example, do we need to run the algorithm several times, and pick a best performance,i.e., in terms of accuracy, f1 ...
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2answers
349 views

LSTM network doesn't converge, what should be changed? [closed]

I'm testing out TensorFlow LSTM layer text generation task, not classification task; but something is wrong with my code, it doesn't converge. What changes should be done? Source code: ...
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1answer
41 views

Metrics of quality of parameter space exploration

Considering a black box optimization problem on non-linear, non-convex function where we want to minimize an objective function. One way to assess the quality of an optimizer is to look at the best ...