Questions tagged [machine-learning]
For questions related to machine learning (ML), a type of algorithm that attempts to "learn" how to perform a task without being given an explicit set of rules to follow in order to perform it. Questions on this site relate to the optimization algorithms that underpin ML, applications of ML in practical settings, and other ways that ML can be used as a tool for OR, and vice versa.
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Can ADMM be applied to "latently coupled" variables?
I've been studying a paper where the authors employ the ADMM in a way that has left me somewhat perplexed. The paper focuses on addressing a robust principal component analysis (RPCA) problem, ...
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Workforce scheduling optimization using ML model output
I am working on a workforce scheduling problem where we want to come up with an intelligent way to identify a set of shift candidates that is just enough to cover all labor demand but also flexible ...
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What is the suitable optimization method for this case?
What is the best optimization method to solve a large-scale problem (about 300 thousand variables)?
The problem is nonlinear, nonconvex, involves only binary variables, and is unconstrained. The ...
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How to show that minimizing the epsilon-insensitive loss is equivalent to a quadratic program with inequality constraints?
This question is about an optimization problem that arises in support vector regression (SVR). Suppose you have $N$ pairs $(\vec{x}_n, y_n)$ as data and would like to find a vector of weights $\vec w \...
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Optimization of a noisy loss function
I'm trying to optimize a noisy loss function (experimental) where the absolute value of the gradient changes significantly depending on the direction taken. In other words, some parameters have a ...
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How to embed an arbitrary graph into (k,d)-kautz space (like multidimensional scaling of non-normed space)
How to embed an arbitrary graph into (k,d)-kautz space (like multidimensional scaling of non-normed space)? See details in the following.
Given a graph $G = \{V,E\}$,
we have a distance matrix (the ...
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An efficient method for zoning bins in a warehouse
Let's assume a warehouse with multiple areas, each including either ground or shelf bins. I want to zone bins in this warehouse such that all bins in a zone are as close as possible. Considering that ...
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Optimization with two constraints using Lagrange multipliers
As a part of an problem where i deploy the EM-algorithm i got stuck with the m-step that can be summarized into the below problem:
Consider the following function: $$f(\alpha_{k,l}, \theta_{n, m}) = \...
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An efficient way to find a postdoc for an operations researcher
Disclaimer: I am not sure if this is the right forum to ask this question.
I am looking for a postdoc position in operations research in the US. So far, I have found three ways:
INFORMS community (or ...
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Training ML models to be used as objectives in optimization problems
Suppose that we have data (in my case, from a chemical process) which includes input data $X$ (characteristic of the material to be processed) and decision data $Y$ (decisions taken by operators to ...
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Logic for Re-Labeling Nodes in a Directed Acyclic Graph
We are currently working at the intersection of metaheuristics and machine learning.
As part of the scheduling problem that we are trying to solve, we have a project network (directed acylic graph) ...
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What can traditional graph cut methods do well, that deep learning cannot?
I have been fascinated by the rise and fall of graph cut algorithms in recent years, which I described in this question: Was there something specific that caused graph cuts to lose popularity in the ...
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Quality of Solutions from Saddle Points vs. Local Minimums
Can Saddle Points Provide "Better Solutions" to Machine Learning Models Compared to Local Minimums?
The solution to a Machine Learning model (i.e. the final model parameters) are selected by ...
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Why does the design of heuristics require considerable domain knowledge?
I am from a machine learning (ML) background and am interested in how ML is applied to Combinatorial Optimisation. As such, as I have been reading around the area and have come across the statement ...
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Using Neural Networks For Solving Optimization Problems
Recently, I came across the below paper and found it very interesting.
Solving Mixed Integer Programs Using Neural Networks; https://arxiv.org/abs/2012.13349
The idea is to use (train with neural ...
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