# Questions tagged [optimization]

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

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### Given a list of integers $\{c_1, \dots, c_N \}$, how do I find an integer $D$ that minimizes the sum of remainders $\sum_i c_i \text{ mod } D$?

I have a set of fixed integers $S = \{c_1, \dots, c_N \}$. I want to find a single integer $D$, greater than a certain threshold $T$, i.e. $D > T \geq 0$, that divides each $c_i$ and leaves ...
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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. ...
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### 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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### Does Retina-net's focal loss accomplish its goal?

Taking out the weighting factor we can define focal loss as $$FL(p) = -(1-p)^\gamma log(p)$$ Where $p$ is the target probability. The idea being that single stage object detectors have a huge ...
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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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### 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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### Why are most commonly used activation functions continuous?

I have come to notice that the most commonly used activation functions are continuous. Is there any specific reason behind this? Results such as this paper have worked on training networks with ...
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### Is it possible to ensure the convergence when training a RNN weight on its SVD decomposition?

I'm reading the following paper in which the author seems to do 2 things interesting: The hidden-to-hidden weight matrix of the RNN is SVD decomposed and train separately. Each orthogonal part of the ...
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### How does the support vector machine constraint imply that sample selection bias will not systematically affect the output of the optimisation?

I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3.4. Support vector machines, the author says the following: 3.4. ...
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### 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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### 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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### 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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### 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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### Is a neural network the correct approach to optimising a fitness function in a genetic algorithm?

I've written an application to help players pick the optimal heroes during the draft phase of the Heroes of the Storm MOBA. It can be daunting to pick from 80+ characters that have synergies/counters ...
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### Is a very powerful oracle sufficient to trigger the AI singularity?

Lets say we have a oracle $S$ that, given any function $F$ and desired output $y$, can find an input $x$ that causes $F$ to output $y$ if it exists, or otherwise returns nil. I.e.: S(F, y) = x \...
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### What does it essentially mean if the neural network has convex error surface?

Suppose if I am building a Linear Regression model with one fully connected layer and a sigmoid with minimizing mean squared error as objective. Why would the error surface be convex? Does finding ...
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### 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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### What is the purpose of the new neurons in the constrained neural network?

I would like to train a constrained neural network. I found a paper on this: https://papers.nips.cc/paper/4-constrained-differential-optimization.pdf. However, I don't really understand how to change ...
31 views

### Any guidance on learning rate / batch size for noisy data (high Bayes error rate)?

Is there any guidance available for training on very noisy data, when Bayes error rate (lowest possible error rate for any classifier) is high? For example, I wonder if deliberately (not due to memory ...
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### Feature visualization on neural networks which are not for classification

Feature visualization allows to better understand neural networks by generating images that maximize the activation of a specific neuron, and therefore understand what are the abstract features that ...
32 views

### Classical Internet routing vs. Swarm routing (such as Ant routing)?

Is it possible to mention the drawbacks/advantages of Swarm routing (such as Ant routing etc) in comparison with classical routing algorithms in communication networks in a general view? In other ...
899 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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### 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 ...
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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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### 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 (...
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### How to make input variable as trainable parameter in a neural network?

I am working on an optimization problem. First, I have done forward training to work the network as a surrogate model, then I freeze the output and I want to find an optimal value of input for a given ...
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### What is the best algorithm for optimizing profit, rather than making predictions?

I am new to machine learning, so I am not sure which algorithms to look at for my business problem. Most of what I am seeing in tools like KNIME are geared toward making a prediction/classification, ...
I've been trying to understand RMSprop for a long time, but there's something that keeps eluding me. $dW$ and $db$ are matrices (that's what I understand from the element-wise comment), so that must ...