All Questions
31 questions
0
votes
2
answers
531
views
Any idea of solving an optimization problem with cubic constraints?
I have the following optimization problem with cubic constraints, which is hard to solve. Are there any ideas, or related references, of solving such a problem?
$$ \begin{array}{ll} \underset {y, z} {\...
CommunityBot
- 1
1
vote
1
answer
84
views
optimization over moving domains
Let $A, B$ be Banach spaces, and for any $a\in A$, $B_a\in B$ is a measurable subset. Consider the following optimization problem:
$$L(a)=\inf_{b\in B_a}\ell(b),$$
where $\ell(b)$ is a infinite-times ...
- 128k
0
votes
0
answers
55
views
Relationship of optimal solutions between the total function and the sub function
This is an unconstrained convex optimization problem. Let $\mathcal{N}=\left\{1,\ldots,n\right\}$, $2\leq n<\infty$. Suppose there are many strongly convex functions $f_i(x)$, where $x\in\mathbb{R}^...
- 5,429
1
vote
2
answers
121
views
How to solve the optimization problem $\max_{\mathbf{w}}\sum_i\text{sign}(\mathbf{w}^T \mathbf{x}_i)$?
I am looking for an algorithm to solve the following optimization problem
$$\max_{\mathbf{w}}\sum_i\text{sign}(\mathbf{w}^T \mathbf{x}_i)$$
where $\mathbf{w}$ and each $\mathbf{x}_i\in\mathbb{R}^d$.
...
1
vote
2
answers
603
views
Maximizing a sum of Gaussians
Let $\mathbf{x}_1, \dots, \mathbf{x}_n \in \mathbb{R}^d$ be $n$ given vectors. Define the function
$$ \mathcal{K}(\mathbf{x},\mathbf{y}) := \alpha\exp\left(-\frac{\|\mathbf{x}-\mathbf{y}\|^2}{2\sigma^...
CommunityBot
- 1
1
vote
0
answers
97
views
How to solve the following optimization problem?
Let $G=(V,E)$ be a connected network with $|V|=n$. Consider the following optimization problem
I'm trying to know under which conditions the following minimization problem has solution :
$${\sum _{i=1}...
- 47
2
votes
0
answers
44
views
Convergent algorithm for minimizing nonconvex smooth function
Let $\Phi$ be the Gaussian CDF and for $\gamma\ge 0$ and $h>0$, define a loss function $\ell_h:\{\pm 1\} \times \mathbb R$ by
$$
\ell_{\gamma,h}(y,y') := \phi_{\gamma,h}(yy') := \Phi((yy'-\gamma)/h)...
- 6,853
3
votes
0
answers
91
views
What is the name for this type of optimization problem?
As we all know, a classic optimization problem can be represented in the following way:
Given a function $f: A \to \mathbb{R}$, find an element $x_0 \in A$ such that $f(x_0) \le f(x)$ for all $x \in ...
0
votes
0
answers
92
views
Optimization problem where the objective function returns a function instead of a real number
As we all know, a classic optimization problem can be represented in the following way:
Given: a function $f: A \rightarrow \mathbb{R}$ from some set $A$ to the real numbers
Sought: an element $x_0 ∈ ...
- 141
2
votes
0
answers
47
views
Why not use global optimization algorithms like PSO to solve decentralized control problems?
I do not see many works that use global optimization algorithms to solve decentralized control problems. Here the decentralized control problem means some entries of the feedback matrix are ...
- 21
2
votes
2
answers
501
views
Limits of argmin ratios and sums
In my research on convergence properties of certain Bayesian methods I have encountered $\mathop{\mathrm{arg\,min}}$ limits of the forms
\begin{equation}
\lim_{n\to\infty} \mathop{\mathrm{arg\,min}}_{\...
- 390
3
votes
0
answers
282
views
Continuum of Lagrange multipliers, duality gap, and minimax theorem
Suppose I have a linear optimization problem involving random variables on some (infinite) probability space $\Omega$. For example, need to maximize expectation $E[Q]$ of random variable $Q$ subject ...
- 781
1
vote
1
answer
189
views
Fritz-John conditions: Equality-constrained case as special case of inequality constraints
In Chapter 4 of Nonlinear Programming: Theory and Algorithms by Bazarra, Sherali, and Shetty, the following claim is made after Theorem 4.3.2 (Fritz-John necessary conditions):
"Note also that these ...
- 6,367
0
votes
1
answer
329
views
Gradient-descent "type" Methods for non-convex and non-smooth functions
Most (stochastic) "gradient descent" type algorithms (such as Nesterov-accelerated gradient-descent or ADAM) seem to be well-defined only for functions which are either:
lower semi-...
- 12.7k
2
votes
0
answers
406
views
Pros and cons of using integer programming alone or combined integer and global optimization?
First, I am not sure if this is the right question to ask in this forum. But I have been looking for answers for a long time and I have been also asking my university's "engineering" professors but I ...
0
votes
0
answers
68
views
Numerically solve a specific saddle-point problem
Let $(\Omega,\mathcal E,\mu)$ be a probability space, $k\in\mathbb N$, $$W:=\left\{w:E\to[0,\infty)^k:\sum_{i=1}^kw_i=1\;\mu\text{-almost surely}\right\},$$ $G$ be a finite nonempty set and $a^{(g)}:E\...
- 167
0
votes
0
answers
95
views
How to maximum L1 norm problem?
I have met a problem these days.
\begin{equation}
\underset{\omega}{\max} \quad \Vert \text{diag}(\mathbf{h}^H)\mathbf{G}^H\mathbf{\omega}\Vert_1 \\
s.t.\quad\mathbf{\omega}^H\mathbf{G}\mathbf{G}^H\...
- 171
0
votes
0
answers
44
views
Is there a multiplier rule for this minimization problem?
Let $(E,\mathcal E)$ be a measurable space, $W\subseteq\left\{w:E\to\mathbb R\mid w\text{ is }\mathcal E\text{-measurable}\right\}$ be a Banach space, $k\in\mathbb N$ and $f:W^k\to[0,\infty)$. I'm ...
- 167
1
vote
0
answers
267
views
Minimum Preserving Transformations [closed]
If $f:X\rightarrow Y$, $g:Y\rightarrow Y$ are functions and $g$ is monotone increasing function then
$$
\operatorname{argmin}_{x \in X} f(x)
=
\operatorname{argmin}_{x \in X} g\circ f(x) .
$$
X and Y ...
- 5,405
0
votes
1
answer
86
views
Linearly constrained saddle-point optimization
Let $f(x,y)$ be a smooth (twice differentiable) saddle function (convex in $x$ and concave in $y$), where $f \colon X \times Y \rightarrow \mathbb{R}$, and $X \subset \mathbb R^n$, $Y \subset \mathbb ...
- 884
1
vote
0
answers
81
views
Maximizing sum of homogeneous functions of order one over a polytope
Let $f_i: \mathbb{R}^n\rightarrow \mathbb{R}$ be
concave, increasing (i.e., if $x\geq y$ where the inequality is entry wise, we have $f_i(x)\geq f_i(y)$), and a
homogeneous function of order one for ...
- 393
4
votes
3
answers
200
views
Maximizing a pseudoconcave function in a box
I am trying to solve the problem:
$\max_{\boldsymbol{s}\in\mathbb{R}^{n}} \frac{\sqrt{\boldsymbol{a}^{T}\boldsymbol{s}+\alpha}}{\boldsymbol{b}^{T}\boldsymbol{s}+\beta}\\
\text{s.t} \;\;0\leq s_{i}\...
- 1,517
1
vote
1
answer
129
views
Optimization problem restricted to a smaller field?
Let $c:\mathbb R^2\to\mathbb R$ be a Lipschitz and bounded function (which can be supposed as "nice" as possible). Let $\mu$ and $\nu$ be two probability measures on $\mathbb R$ with finite first ...
CommunityBot
- 1
3
votes
0
answers
239
views
Constrained optimization with a Proportional-Integral-Derivative (PID) controller
My engineering colleagues have devised an interesting approach to equality-constrained optimization. I.e. they wish to solve the problem $\min_x f(x)$ subject to the constraint $g(x) = 0$ where $f, g ...
- 163
0
votes
0
answers
42
views
What (analytical or numerical) method can I use to solve scalar optimal problem?
I got the following optimization problem in mind and I am looking for some (analytic or numerical) methods to solve it. Can anyone have any ideas? Here is problem
\begin{aligned}
& {\text{...
- 221
1
vote
0
answers
232
views
Semi-convex problem and almost convex problem
I have a target function, I've computed its Hessian to check convexity, it has a positive-definite sub-matrix and small negative-definite sub-matrix and a kernel. Sometimes it is even better -- the ...
- 355
1
vote
0
answers
94
views
About a particular definition of a Hessian of a function of tuples of matrices
Say I have a function $L : (W_1,..,W_{H+1}) \rightarrow \mathbb{R}$ i.e it takes a tuple of $n$ matrices of different dimensions and computes a number from them.
Then I see being defined a ...
- 2,246
2
votes
0
answers
354
views
Likelihood convexification
I am doing constrained vector optimization using a non-convex non-linear likelihood function. My problem is of the following form:
$$\begin{align*}\hat Q &= \underset{\vec Q}{\arg\min} -\log \...
1
vote
0
answers
100
views
Changing a nonlinear equality constraint into some conic inequality plus rank constraint
If we have a constraint optimization problem in which one of our constraint is $\prod\limits_{k = 1}^N {\left( {x - {a_k}} \right) = 0} $ . How could this nonlinear equality condition be changed into ...
- 33
5
votes
1
answer
3k
views
Maximizing supermodular functions
I have a real supermodular objective function which I want to maximize with constraint. The constraint is on the size, like |A|=k .
I am wondering if anyone can give me more information about a ...
4
votes
1
answer
2k
views
lipschitz constant of a multivariate function
I have a function $f:\mathbb{R}^{50} \rightarrow \mathbb{R}$ and I need to compute the Lipschitz constant of $f$ to solve an optimization problem using a specific algorithm. Does any one have ...
- 28.6k