Questions tagged [convex]
A convex set includes all points lying between any two points from the set. A convex function on such a set is a function lying below any straight line connecting two points from its graph. Convex optimization is concerned with searching for the minimum of such a function.
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Why backpropagation if loss function is not convex in nature?
Backpropagation contains the method of gradient decent, which works well for convex loss functions with a global minima.
But, for training, in most of the neural network tasks, backpropagation is ...
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19 views
Online Optimization - Regret in Absolute Error
In the online convex optimization literature static regret is defined as $\sum_{t=1}^{T}\left(f_t\left(x_t\right)-f_t\left(x^*\right)\right)$ where $x^*=\arg min_{x\in\mathcal{X}}\sum_{t=1}^{T}f_t\...
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67 views
Convexity of conditional expectation
Define $g(k)\equiv\mathbb{E}(X|_{X>k})$ and assume that the probability density $f$ of $X$ is twice continuously differentiable. Is there a sufficient condition in terms of $f$ that imply that $g^{...
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19 views
Hard margin SVM: existence of KKT point?
I'm learning about support vector machines and ran into a question while going through the hard margin case.
First I'll have to go through some steps in the problem to arrive where at I'm confused. ...
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1answer
188 views
Prove that a function $\ln (e^{a_1} + e^{a_2} + \cdots + e^{a_n} )$ is convex?
Let $f(a_1, a_2, · · · , a_n) = \ln (e^{a_1} + e^{a_2} + \cdots + e^{a_n} )$. Show that $f$ is convex.
Now, to show that is a function is convex, we can take second derivative of the function and if ...
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1answer
63 views
How to recover primal problem from its dual counterpart
I am asking this from context of optimization in machine learning. We often talk about a primal problem and how this primal problem can be solved by first converting it into a dual problem (Using ...
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15 views
How to get precise answer from stochastic gradient descent
I have a convex optimization problem in few variables and I have an unbiased estimator of the gradient without having the ability to evaluate the function itself. I want to do gradient descent but the ...
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73 views
How positive definite Hessian approximations for SGD (e.g. Gauss-Newton) handle saddles?
For example due to symmetry of parameters, functions optimized in machine learning usually have huge number of local minima and saddles - growing exponentially with dimension.
I am trying to ...
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27 views
How to solve a non-convex with equality constraint optimization problem?
I have a non-convex optimization problem with equality constraint, I can derive the KKT conditions, but it seems just one of the KKT conditions is valid. Could you please give some advice on how to ...
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2answers
205 views
Convexity of cross entropy
I am not sure if this is a better fit for this site or mathematics.stackexchange but I've seen similar questions on here before. I'd like to know if the following is true and if so, how I could go ...
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23 views
Why H(y) is a concave function of p(x) when p(y) is a linear function of p(x)?
I got stuck in the line when reading a theorem in The Elements of Information Theory on page 59:
If p(y|x) is fixed, then p(y) is a linear function of p(x). Hence H(Y),
which is a concave ...
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31 views
Normal equations issue
Let $A\mathbf{x}=\mathbf{b}$ be an overdetermined system, with $A$ being an $n \times m $ full-column rank rectangular matrix.
Are these minimization problems equivalent?
$$ 1) \;\underset{\mathbf{x}...
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29 views
Coordinate descent in integer programing: when does it work?
Denote $N_i=\{0,1,\dots,\bar{n}_i\}$ and define $N=N_1\times \dots \times N_I$. I want to minimize a function $f:N\rightarrow \mathbb{R}$. For the functions $f$ that interest me, it is very easy to ...
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1answer
117 views
Is error function always assumed and convex?
While updating weights of the neural network, most of the algorithms use convex optimisation because of the reason that error is a convex function.
My doubt is that whether the convex-ness of error ...
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1answer
62 views
How to understand whether Stochastic Gradient Descent has converged?
I am using SGD to solve for MSE function. My training set is around 50K, and I am monitoring the gradient at every epoch (once a pass is completed over all the training data). I played around a lot ...
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1answer
120 views
Regularizing the inverse coefficient matrix in multivariate regression
I'd like to minimize the objective
$ \operatorname{tr}[ (Y-XR^{-1})^T (Y-XR^{-1}) ] + \lambda \sum_{ij} |R_{ij}|$
wrt to $R$ (which is $P \times P$ but non-symmetric) where $Y$ and $X$ are both $N \...
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23 views
How to diagnose why numeric solver is not converging? [closed]
Are there specific approaches, methods or software that help in determening why an optimum is not found for a particular optimization problem?
For example, the solution landscape could be visualized ...
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70 views
Optimization textbooks for statistics and data analytics
Any statistical analysis, machine learning or data science involves some sort of optimization at the end of the day.
I'm looking for good linear and nonlinear optimization textbooks for self ...
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6answers
4k views
For convex problems, does gradient in Stochastic Gradient Descent (SGD) always point at the global extreme value?
Given a convex cost function, using SGD for optimization, we will have a gradient (vector) at a certain point during the optimization process.
My question is, given the point on the convex, does the ...
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0answers
122 views
Vapnik-Chervonenkis Dimension
My question has to do with the VC dimension of the class of convex polygons with $m$ vertices. A solution to this problem is given in the following: Advanced Algorithms.
Call the class of all ...
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1answer
861 views
Any optimization problem can be expressed as one with a linear objective
For any standard optimization problem(think linear programming, convex, non-convex problem), would it be possible to express the optimization problem as one with a linear objective?
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1answer
68 views
Why is global convexity not possible in higher-dimensional settings for this loss function?
I was reading this paper which discusses nonconvex penalized regression. They use the following notation:
$X\in\mathbb{R}^{n\times p}$ is a data matrix with $n$ observations in $p$ variables
$\beta\...
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0answers
32 views
Why proximal gradient is a special case of sub-gradient?
While I was learning proximal gradient descent methods, I saw many similar statements as below:
$$p=prox_f(x) \Longleftrightarrow x-p \in \partial f(p) $$
for example, ...
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1answer
354 views
What is the use of the log of the sum of exponents in machine learning
I want to understand why somebody would use log sum exponent trick.
I am reading this blog.
But I don't really understand the first paragraph.
It says
Let's say we have an n-dimensional ...
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22 views
Low-variance estimate for the mean of the sotfmax transformation of a variable
Consider a set of infintiely-differentiable convex functions real-valued functions $f_i: \mathcal X \rightarrow \mathbb R$, where $i$ varies from $1$ to $m$, and suppose
we know all the moments of $...
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1answer
122 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}
...
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0answers
91 views
Is cross-entropy loss for MaxEnt models convex? If so, how do I prove it?
I am trying to prove if cross-entropy loss for MaxEnt models is convex.
My first attempt at approaching this problem is to compute the Hessian matrix of second-order partial derivatives and showing ...
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1answer
99 views
Is the difference between two iid log-concave distributions still log-concave?
Assume two iid random variables X and Y, with continuous and differentiable pdf $f$ and cdf $F$. Let Z=X-Y. Is the pdf of Z log-concave?
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1answer
131 views
Restriction of concave function to bounded convex set is concave?
Suppose I have a function that is concave, and has as its domain $R^n$. If I restrict the function to a bounded convex set, say the probability simplex, then the resulting function should still be ...
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6answers
9k views
Why study convex optimization for theoretical machine learning?
I am working on theoretical machine learning — on transfer learning, to be specific — for my Ph.D.
Out of curiosity, why should I take a course on convex optimization?
What take-aways from convex ...
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2answers
382 views
Variance of the reciprocal of a strictly positive random variable
In this post it is stated that due to Jensen's inequality the expected value of the reciprocal of a strictly postive random variable $X$ will satisfy:
$$\mathbb{E}\left[\frac{1}{X}\right] \geq \frac{...
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0answers
104 views
KKT condition in layman's term
The most intuitive explanation for KKT condition I can find is this post, which is still too complex to me. Is there any more intuitive explanation? How to introduce this to a layman?
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1answer
56 views
Why does Jensen's imply this?
Let $F$ be a convex function. If $Y$ and $Z$ are independent random variables and $EZ=0$, then $$EF(Y) = EF(Y+EZ)\leq E(Y+Z).$$
I fail to understand why the last inequality is true. Can someone ...
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1answer
203 views
Is the log loss function $f(w) = y_t \log(y_p) + (1 - y_t) \log(1 - y_p)$ convex in $w$? [duplicate]
Kaggle defines the log loss function as: https://www.kaggle.com/wiki/LogarithmicLoss
$$f(w) = \log \Pr(y_t|y_p) = y_t \log(y_p) + (1 - y_t) \log(1 - y_p)$$
Let $y_t \in {0, 1}$, and $y_p$ is given ...
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1answer
99 views
convexity of two convex function combined
If I have such an optimization problem:
$$
\arg\min_{x,z} \dfrac{||y-x||_2^2}{z}+z(||y-x||_2^2)
$$
where $y$ is known and $z>0$.
If $z$ is fixed, then this function w.r.t $x$ is convex. Similarly,...
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0answers
36 views
Convex subspaces and zero covariance?
$\DeclareMathOperator*{\argmin}{arg\,min}
\newcommand{\E}{\text{E}}$
This is from the Analytics Iowa LLC notes provided at http://www.public.iastate.edu/~vardeman/stat602/StatLearningNotesII.pdf, p. ...
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3answers
2k views
Is PCA optimization convex?
The objective function of Principal Component Analysis (PCA) is minimizing the reconstruction error in L2 norm (see section 2.12 here. Another view is trying to maximize the variance on projection. We ...
2
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1answer
282 views
Gaussian Processes for convex functions
As I understand, when using Gaussian Processes (GP) for regression, one can/should incorporate prior knowledge about the function into the GP. Let's say I have a nonlinear function $f: \mathbb{R}^n \...
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2answers
3k views
Logistic Regression is a Convex Problem but my results show otherwise?
I know that logistic regression is a convex problem. Furthermore, from Lemma 1.17 in these optimization lecture notes, if a function $f : \mathbb{R}^n \rightarrow \mathbb{R}$ is convex, then the ...
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1answer
4k views
Why is the cost function of neural networks non-convex?
There is a similar thread here (Cost function of neural network is non-convex?) but I was not able to understand the points in the answers there and my reason for asking again hoping this will clarify ...
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2answers
87 views
normalization constraint in support vector machine
A support vector machine initially poses the following optimization problem:
$$max_{\gamma, w, b} \gamma \\
s.t\ \\
y^{(i)}(w^Tx^{(i)} + b) \ge \gamma,\ \ i=1,\dots,m \\
||w|| = 1$$
I understand the ...
4
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1answer
322 views
Online convex optimization: Why use strongly convex regularizer for regularized-follow-the-leader instead of strictly convex (or just convex)
I've read through Hazan's paper on online convex optimization. I don't quite understand why the regularization term must be strongly convex instead of more relaxed condition such as strictly convex.
...
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2answers
1k views
Logistic regression cost surface not convex [duplicate]
I am building a simple logistic regression model on 2D data. Here is the input I use.
I built a logistic regression model using this data and it successfully is able to find the discriminating line ...
2
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1answer
454 views
Derivation of the Iterative Reweighted Least Squares Solution for $ {L}_{1} $ Regularized Least Squares Problem
I'm trying to fitting a line with IRLS with L1 norm, but I'm struggling to understand why my idea is wrong.
1 - init the weights $w$
2 - fit with simple LS and obtain a starting model $\beta_0$
3 - ...
5
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2answers
1k views
In online convex optimization, what is a leader in FTL algorithm?
I am currently reading into online convex optimization. Can someone please explain me what exactly is a leader in the Follow-The-Leader algorithm and its variants?
Why is it called Follow-The-Leader?...
2
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1answer
225 views
Writing Random variable $X$ as a convex combination of $a$ and $b$
When proving the Hoeffding's lemma on Wikipedia, it says
Next, recall that $e^{sX}$ is a convex function on the real line:
$\forall X \in [a,b]: $ $e^{sX} \le \frac{b-x}{b-a}e^{sa} + \frac{x-a}...
3
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2answers
543 views
Convexity, linearity and their combination for MLE
I'm going through Murphy's ML a Probabilistic Perspective book and in chapter 9 we have the following excerpt talking about the MLE of exponential family distributions:
My question is: How do we ...
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1answer
76 views
Difference between minima of L1-regularized quadratics
How can I find
$$F(A,b,x,c)=\inf_{\theta\in\mathbb{R}^n}(\theta^{\top}A\theta+b^\top\theta+x^\top\theta+c||\theta||_1)-\inf_{\theta\in\mathbb{R}^n}(\theta^{\top}A\theta+b^\top\theta-x^\top\theta+c||\...
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1answer
175 views
Relating $f(\mathrm{Var}[X])$ to $\mathrm{Var}[f(X)]$ for Positive, Increasing, and Concave $f(X)$
The arrival of photons at a pixel in an image sensor is a Poisson distributed random variable such that the input can be modeled as a Poisson r.v. $X\sim \mathrm{Poisson}(\lambda)$.
Since the input ...
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1answer
389 views
Is a linear discriminant function actually “linear”?
Chris Bishop introduces the multi-class linear discriminant functions (a.k.a. linear machine) in PRML as follows (p. 183):
In proving the convexity of decision regions, it's assumed that $y_k(\cdot)$ ...
