# Questions tagged [optimization]

In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint density (or mass function) of the observed data referred to as Maximum Likelihood Estimation (MLE).

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### How do I know that my weights optimizer have found the best weights?

I am new to deep learning and my understanding of how optimizers work might be slightly off. Also, sorry for a third-grader quality of images. For example if we have simple task our loss to weight ...
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### Discussion about modern deep learning training strategies

Previously I have put a lot of effort into training networks appropriately. However, talking to colleagues, a lot of the things I did may be redundant due to novel optimizers and the theory of deep ...
1 vote
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### Strategy to choose maximum value from an unknown array of n numbers

Suppose you have an array of n normally distributed numbers whose values are initially unknown(and the probability parameters are unknown too). You must choose one number and you want it to have ...
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### Optimization of a dispatch strategy

I am working at a project where I have to optimize the makespan of a system. The briefly description of the system: -I have an Input Output Machine which sends tubes to analyzers which has to execute ...
15 views

### Jenks goodness of variance fit - Interpretation

I am working on clustering/grouping 1D data. I am trying to find bins of multiple variables seperately. So, I tried the jenks natural breaks algorithm. Based on the ...
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### Recent research on solving "inverse" ODE problems with neural networks?

I come from a physics background, and I am not familiar with the state-of-the-art research in solving ODE optimization problems with NNs. Let me briefly introduce this so-called "inverse" ...
1 vote
16 views

### Hyperparameter Tuning Guidelines Across Lots of Models

I am performing a set of experiments in which I need to tune a fairly small set of hyperparameters but over a very large space of models trained on relatively related datasets and I'm trying to ...
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### Isotonic regression with constraints on X threshold spacing

I am experimenting with using https://scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html to approximate some noisy monotonic data. The generated curves have "jumps&...
• 101
1 vote
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### modelling- Regression tree with constraints on depth and monotonicity

My goal is to build a regression tree model that takes as input (the z-score: the odds of the logistic regression) and a binary variable (default/non default) as target but with constraint on the ...
19 views

### How to solve Nonlinear least squares problem?

Initial idea is to use euclidean distances. But I do not understand how should I solve this task.
57 views

### Optimize a non-linear function in Python

I am trying to optimize a function using scipy.optimize, but it does not converge. I have a trading strategy with a default stop-loss based on the lowest price over 20 days. I want to optimize this ...
12 views

### Gradient descent/Adam converging to suboptimal solutions

I am using neural nets to find the minimum of a complex function to which I compute the mean (crit in my code). Here is my net : ...
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### Plot six variables

I would like to plot a landscape spanned by six variables. The numerical target variable is explained by five numerical variables. Ultimately, it is about to get a visual impression for optima and the ...
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