Questions tagged [constraint]
The constraint tag has no usage guidance.
87
questions
0
votes
1answer
21 views
Parametrization of a skew-normal distribution such that negative part is constant
I was wondering, how the parameters of the skew-normal distribution (https://en.wikipedia.org/wiki/Skew_normal_distribution) would be constrained when I require that a constant part of its support is ...
3
votes
0answers
17 views
Sampling from Gaussian distribution subject to a quadratic inequality constraint
Would it be possible to generate samples $x \in \mathbb{R}^n$ from $\mathcal{N}(\mu, \Sigma)$ subject to an inequality constraint $x^\top Q x/2 + b^\top x \le c$, $Q = Q^\top \succeq 0$. We also know, ...
1
vote
0answers
20 views
Is there a way to run constrained EM?
I would like to run an EM (expectation maximization) algorithm to estimate hidden Markov model parameters except, in my case, I have an extra constraint on my start and final states in my HMM.
How do ...
0
votes
0answers
40 views
Solving ridge regression for p >> n case using dual algorithm with or without nonnegativity constraints
I was reading the paper
"Efficient Regularized Regression with
L0 Penalty for Variable Selection and Network Construction"
in which iterated ridge regression is used to solve L0 penalized regression ...
2
votes
1answer
29 views
Confidence limits on a curve fit with algebraically related parameters
I suspect that the required techniques for my question already exist, but I don't know what the correct nomenclature is - if so apologies, and can someone point me at the right resources?
I have a ...
3
votes
1answer
55 views
multiple regression with constraints of independent variables
I'm running a regression analysis with independent variables $X_{1}, X_{2}, \cdots, X_{n}$ and dependent variable $Y$. There is a constraint among some of the independent variables, say, $X_{1} + X_{2}...
0
votes
0answers
11 views
Speed of transition parameter constraints
Given a logistic smooth transition regression
\begin{equation}
y_{t}=x_{t}^{\prime }\beta _{1}(1-{g}(z_{t};\gamma ,\delta
))+x_{t}^{\prime }\beta _{2}{g}(z_{t};\gamma ,\delta )+\varepsilon _{t}%
\text{...
1
vote
0answers
20 views
Sum of residuals in constrained linear regression
Does sum of residuals == 0 hold even if we add ordinality constraints for the coefficients in a regression model?
It would be great if someone can point out to any literature (paper/presentation/...
2
votes
0answers
48 views
How to handle real-world (soft) constraints in an optimization problem? [closed]
I am working on a problem which involves optimizing for minimum power consumption in a large compressor network interconnected through pipelines (think of a connected graph with nodes as compressors ...
1
vote
1answer
72 views
How to implement constraints in a neural network for a regression problem?
Let's say I have different sensors in an engine, and I make a neural net which predicts the engine's temperature given different operating conditions measured by the sensors. I happen to know the ...
0
votes
1answer
64 views
contraints on parameters in MCMC
I run a basic version of mcmc, based on example given in this Metropolis-Hasting tutorial
...
0
votes
0answers
11 views
Sample subsets with distribution proportional to elementwise product
I have a set of $n$ elements, $x_1,x_2,\dots,x_n$, each with a weight $w_1, w_2,\dots, w_n$. I want to sample a subset of fixed size $k$ such that the probability of drawing $\{x_a,\dots,x_b\}$ is ...
0
votes
2answers
76 views
What are some of the approaches of defining joint pdf under sum constrain?
For example, we have three random variables,
$x_1 = Uniform(10,20)$
$x_2 = Uniform(20,40)$
$x_3 = Uniform(50,150)$
which follows the condition $x_1+x_2+x_3 = 100$
I am looking for a joint ...
0
votes
0answers
12 views
Timeseries space consumption optimization for machine learning model
I need to train machine learning model that can run on device with low memory. My features come from aggregations of time-series so I need to store all data in this time-series until it leaves the ...
0
votes
0answers
35 views
GMM: can a constrained regression perform better than an unconstrained regression?
I have one linear regression with a set of explanatory variables and an other set of instrument variables.
In my constrained regression, one of the coefficient for a given variable is constrained to ...
2
votes
2answers
98 views
Normal distributed random variables with constraint?
Consider $n$ random variables $X_i$ with $i=1,2,...,n$, each drawing values from identical normal distributions with mean $\mu=0$ and standard deviation $\sigma=const.$ so that expectation values are $...
1
vote
0answers
20 views
Constraints on low dimensional representations of data
Is there literature discussing introduction of constraints to loss functions in order to specify certain structures on low dimensional representations? If so, how do they compare the efficacy of the ...
1
vote
1answer
26 views
Clustering with constraint on minimum size of cluster
I have dataset of $n$ objects, I want to cluster them according to correlation and I want to divide the dataset into groups of similar objects of sizes not less than 50 - because I use clustering for ...
0
votes
0answers
32 views
Accelerated Projected SGD under box constraints
Are there generalizations of ADAM or Adagrad algorithm that allow box constraints for the parameters to be incorporated in the gradient descent step? Is it valid to simply run the algorithm as usual ...
1
vote
0answers
23 views
significance in linear regression with constraints
I have a problem which is similar to linear regression, but differs in two main points: 1) the number of regressors is equal to the number of observations and 2) I have constraints on the regressors.
...
0
votes
1answer
237 views
How do I design this multi-objective fitness function for use with genetic algorithm?
I have a multi-objective optimization problem that I am applying genetic algorithm (GA) to solve. Currently, there are only 2 objectives:
minimize cost
maximize validity
The cost minimization is ...
0
votes
1answer
29 views
Correlation of Vectors with Constrained Sum
Taking the correlation of vectors with a constrained, fixed sum (say, a simplex, where sum is always 1) will induce spurious negative correlations, since increasing one element always means decreasing ...
0
votes
0answers
48 views
Multilabel classification problem where a number of output labels are in certain range
I am tackling a multi-label classification task.
In this problem,
we have 13 classes
For one sample $x \in R^{20}$, $2 \leq |h(x)| \leq4$
Here, $h(x)$ means the prediction labels of sample x by ...
2
votes
0answers
37 views
Does this distribution family exist?
Let $D$ be a one-parameter distribution family whose support is the positive reals. Let $D_x$ be a distribution from this family parametrized by its mean $x \ge 0$, let $X \sim D_x$ and $Y \sim D_y$ ...
0
votes
0answers
97 views
How to penalize change of states in Hidden Markov model?
I'm trying to fit a HMM on a sequence of observations and I would like to introduce some constraints that would penalize an excessive number of changes of state in the complete sequence (where "change"...
1
vote
0answers
28 views
Minimal requirement for reaching a feasible solution in non-convex constrained gradient descent
The problem is as follows:
$\max_x f(x) \enspace , \enspace \text{s.t.} \enspace g(x) \leq \alpha $
We can not assume that either $f$ nor $g$ are convex, on the contrary - we can assume they are ...
0
votes
0answers
47 views
Restriction in bayesian likelihood expression
I'm doing MCMC simulation but I'm confused in some part of my model. I dont know which of my likelihood expression is right.
My model is as following.
$\gamma$ = $(\gamma_{1},\cdots,\gamma_{K})$ and ...
0
votes
2answers
583 views
How to use equalities as constrains with constrOptim in R
I want to solve a matrix system which have several solutions (infinite since it is overdetermined - 6 equations with 8 unknowns). However, the way I want to do it is to a criterion for the variables, ...
2
votes
2answers
464 views
Coordinate descent with constraints
When performing constrained optimization on a smooth, convex function using coordinate descent, for what types of constraints will the algorithm work ? (i.e. converge or reach an approximate optimum ...
1
vote
0answers
56 views
2
votes
1answer
119 views
L2 SVM (squared hinge) theory
The linear L2 SVM can be intuitively understood as
\begin{equation}
\text{minimize } f(\boldsymbol{w}) = \frac{1}{2} \Vert\boldsymbol{w}\Vert^2_2 + C \sum_{i=1}^m \xi_i^2 \tag{1}
\end{equation}
...
5
votes
3answers
187 views
Generating identically-distributed random variables with a constraint
Is there a way to generate identically-distributed random variables (eg $x_1,x_2,x_3,x_4$) with the following constraint:
$\frac{x_1*x_2}{x_3*x_4} ≡ 1$
$x \in (0,1)$
Please note that simply ...
2
votes
0answers
74 views
Eigenvalue-constrained covariance estimation and AIC
I'm estimating a covariance matrix $\Sigma$ from datasets where the number of dimensions $p$ may be close to or larger than the number of data points. To obtain a well-conditioned estimate, I use the ...
0
votes
0answers
149 views
Maximizing profit, using GAMS?
I've been given the following optimization problem and this is what I have done so far:
Cuppa Coffee Company mixes specialty coffee blends to sell to SmartBux, a small
chain of coffee shops. The ...
0
votes
1answer
44 views
How to compute a non linear mix model with links between the parameters and their variance in R?
I'm working on a biological problem where we want to modelize the impact of a treatment and a chemical modification on the measured quantity of a molecule (peptides).
We will note $x_{ijk}$ the ...
1
vote
0answers
59 views
Constrained classification - how to classify a set of related items
I need to classify a set of elements into a few categories.
Example:
...
0
votes
1answer
49 views
Primal constrained (not on $\xi_i$, but on $\mathbf{w}$!) formulation of L2 SVM
I recently came across a statement that the unconstrained primal L2-regularized SVM formulation
$$
\min_\mathbf{w} \lambda \|\mathbf{w}\|^2_2 + \sum_i \max (0, \; 1 - y_i \mathbf{w}^T\mathbf{x}_i)
$$
...
0
votes
1answer
217 views
Monotonic constraints in regression model with interaction
I'm trying to figure out what constraints I need to use to have monotonicity (both on $x$ and $z$) on the regression model with interaction. My model is:
$$\mathbb{E}[Y|x,z]= \beta_0+\beta_1x+\...
0
votes
1answer
152 views
R: Genetic Algorithm supporting dynamic constraints
Is there any package available or some other approach to implement constraints like (x1 < x2) or even more complex relationships provided by some function.
Another desired option would be a ...
1
vote
1answer
606 views
Reinforcement learning (Q-learning) under constraint
I want know how to add a constraint to Q-learning. I have an action resulting in two rewards every time (reward 1= delivery cost , reward 2= delivery time). I want to minimize the cost while ensuring ...
0
votes
0answers
342 views
How can I impose such constraints on regression coefficients in practice?
I am estimating a linear regression model with the dependent variable $y$ and $k$ explanatory variables $x_1, \ldots, x_k$:
$$y=\beta_0+x_1\beta_1+ \ldots+ x_k\beta_k + \epsilon.$$
In the estimation ...
1
vote
1answer
126 views
Fitting a regression with constraints on the relationship between one independent variable and the dependent variable?
Let's say I have a dependent variable y (a rating of the pleasantness of shopping at a particular store from 0 to 100), and 10 independent variables ...
1
vote
1answer
46 views
Stan - find dimensions of an object - lower and upper question [closed]
I have a bunch of objects (roughly rectangular) , for some of which I know what their dimesions - x, y, and ...
2
votes
0answers
102 views
Constraints on the moments of a bounded probability distribution
Consider a probability distribution with support on $[0,1]$. Suppose the first $n$ raw moments $m_1,...,m_n$ are given. What are the constraints on the $(n+1)^\text{th}$ moment? Obviously we have the ...
4
votes
0answers
37 views
Algorithm that preserves the order of the predicted variable
Hi all,
I need some advice on possible algorithms that I can apply to the following problem (if possible with pointers to implementations of these algorithms).
The dataset:
I have some dataset ...
1
vote
2answers
575 views
Is there a clustering algorithm that can take a maximum distance from any mean as a constraint?
I am building an analytical tool that depends on being able to take a bunch of 1 dimensional numbers and group them into categories based on how close each number is to the mean of the group. However, ...
1
vote
1answer
83 views
Zero restrictions in state-space models/ Kalman Filter
I am estimating a state space model using Kalman and the EM algorithm in Matlab, using Kevin Murphy's toolbox (http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html). My question should in ...
0
votes
0answers
25 views
Clustering with Parametric distance in terms of error
I want to solve a constraint-based clustering problem. The constraint is a function of the points in one cluster. In simpler terms, I want to divide the data points into two clusters (A and B), such ...
0
votes
1answer
214 views
Two-way ANOVA with modified sum-to-zero constraint
I have a dataset with two categorical predictors and a continuous response. Suppose the predictors have $a$ and $b$ levels, respectively, with group-specific parameters $\alpha_1, \dots, \alpha_a$ and ...
1
vote
0answers
240 views
Understanding constraints in regression / maximum likelihood
There are many questions about implementing parameter constraints. My question is about understanding constraints. What follows is a very contrived example that I thought up on the spot. Feel free to ...
