# Questions tagged [optimization]

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

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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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### 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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### How to optimize a function using a genetic algorithm?

I recently learned about Genetic algorithms and I solved the 8 queens problem using a genetic algorithm but I don't know how to optimize any functions using a genetic algorithm. I want a guide on how ...
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### 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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### Is a calculus or ML approach to varying learning rate as a function of loss and epoch been investigated?

Many have examined the idea of modifying learning rate at discrete times during the training of an artificial network using conventional back propagation. The goals of such work have been a balance ...
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### Loss jumps abruptly when I decay the learning rate with Adam optimizer in PyTorch

I'm training an auto-encoder network with Adam optimizer (with amsgrad=True) and ...
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### Genetic Algorithms: Trade-off between time and variance with regards to fitness function

I'm developing an AI to play a card game with a genetic algorithm. Initially, I will evaluate it against a player that plays randomly, so there will naturally be a lot of variance in the results. I ...
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### How do I compute log-likelihood for training set in supervised learning?

I am building a supervised learning model and I wish to compute the log-likelihood for the training set at the point of the minimum validation error. Initially, I was computing the sum of all the ...
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### Optimization step in Apprenticeship Learning via Inverse Reinforcement Learning

Why the optimization step of the algorithm a quadratic program? [See: Apprenticeship Learning via Inverse Reinforcement Learning; page 3] Isn't the objective function linear? Why don't we treat ...
51 views

### Maximizing or Minimizing in Trust Region Policy Optimization?

I happened to discover that the v1 (19 Feb 2015) and the v5 (20 Apr 2017) versions of TRPO papers have two different conclusions. The Equation (15) in v1 is $\min_\theta$ while the Equation (14) in v2 ...
319 views

### How can we calculate the gradient of the Boltzmann policy over reward function?

I'm struggling with an inverse reinforcement learning problem which seems to appear quite often around the literature, yet I can't find any resources explaining it. The problem is that of calculating ...
141 views

### If Deep Learning is non convex, then why use convex loss?

I was just reading through some convex optimization textbooks to hopefully improve my deep learning understanding and come up with new ideas. Halfway through, I decided to Google a bit! It's obvious ...
67 views

### Can TensorFlow minimize “symbolically”

From https://stackoverflow.com/questions/36370129/does-tensorflow-use-automatic-or-symbolic-gradients, I understood TensorFlow requires all the operations in the Graph to be explicit formulas (instead ...
570 views

### Input optimization on a supervised learning system

Problem Given a collection of pairs (X, y) where X belongs to R^n and y belongs to R, find the X such that the associated y would be maximum. Example Given: (X=(1, 2), y=-9) (X=(-2, 4), y=-36) (X=...
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### 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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### 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. ...
2k views

### 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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### Application of Ai to task scheduling problems on heterogenous platforms

Let's say we have a cluster of 20-2000 heterogenous compute nodes. Consider for example the parallel solution of the helmholtz equation: Now we want to distribute the solution process and, to make ...
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### Should the mutation be applied with the hill climbing algorithm?

As far as I understand, the hill climbing algorithm is a local search algorithm that selects any random solution as an initial solution to start the search. Then, should we apply an operation (i.e., ...
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### Which features and algorithm could optimize this air-conditioner problem?

Imagine we have 2 air conditioner systems (AA) and 2 "free cooling" systems which mix external and internal air (FC) in a closed box which always tends to warm up. For each system, we have to find ...
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### Which algorithm would you use to solve a multiple producer-consumer problem with constraints?

I'm solving this problem similar to consumer-producer of materials (i.e. sand). This is the graph of the problem: Where Req (...
607 views

### Why does 'loss' change depending on the number of epochs chosen?

I am using Keras to train different NN. I would like to know why if I increment the epochs in 1, the result until the new epoch is not the same. I am using shuffle=False, and np.random.seed(2017), and ...
242 views

I have followed the pseudocode in the ADADELTA paper (top right on page 3), and wrote the following Python code for solving the optimization problem L(x) = x^2: ...
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### Reproduce Firefly Algorithm experiments of original paper?

I have been trying to reproduce the experiments done in the original: "Firefly Algorithm for multimodal optimization" (linked in the question) so far: unsuccessfully. For the moment being I'm okay if ...
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### Knapsack of mixture with constraints

I'm trying to find the optimized mixture for a specific set of substances. Each of those substances have characteristics that I want to optimize in the mixture (some characteristics I want to minimize ...
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### Would a sentient AI try to create a more optimised AI which would eventually overtake AI 1.0?

Would AI be a self-propogating iteration in which the previous AI is destroyed by a more optimised AI child? Would the AI have branches of it's own AI warning not to create the new AI?
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### Are FFNN (MLP) Lipschitz functions?

My question is regarding standard dense-connected feed forward neural networks with sigmoidal activation. I am studying Bayesian Optimization for hyper-parameter selection for neural networks. There ...
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### What are Hyper-heuristics?

I wanted to know what the differences between hyper-heuristics and meta-heuristics are, and what their main applications are. Which problems are suited to be solved by Hyper-heuristics?
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### How to avoid falling into the “local minima” trap?

How do I avoid my gradient descent algorithm into falling into the "local minima" trap while backpropogating on my neural network? Are there any methods which help me avoid it?
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### Is there a way to define the boundaries of the optimal size of a training set?

At a related question in Computer Science SE, a user told: Neural networks typically require a large training set. Is there a way to define the boundaries of the "optimal" size of a training set ...