Questions tagged [convex-optimization]

Convex optimization, a subfield of optimization, studies the problem of minimizing convex functions over convex sets. The convexity property can make optimization in some sense "easier" than the general case - for example, any local minimum must be a global minimum.

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Convex Maximization with Linear Constraints

I am doing active research in convex maximization w.r.t. linear constraints. There are many cases which can be efficiently approximately solved, e.g., convex quadratic maximization, log-sum-exp ...
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9 votes
4 answers
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What is the best open source solver for large scale LP optimization in pyomo?

I have used Gurobi and cplex for solving large scale LP problems with Pyomo. However, I do need to use open source solver. Any advise? glpk and cbc seems to be very slow in solving the problem (with ...
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8 votes
4 answers
421 views

Disciplined convex programming representation of $x\sqrt{1-x}$

Anyone have an idea for a disciplined convex programming (DCP) representation of the concave function $x\sqrt{1-x}$, which is defined over the domain $[0,1]$? The Taylor series about $x=0$ is $$x - \...
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8 votes
1 answer
254 views

Convexity/Concavity of Average Number of Jobs in M/M/1 Queue?

I am working on a problem involving the average number of jobs $L$ in an M/M/1 queue with arrival rate $\lambda$, service rate $\mu$. For traffic intensity $\rho = \frac{\lambda}{\mu}$, $$ L = \frac{\...
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8 votes
1 answer
210 views

Maximize correlation subject to nonconvex correlation constraints

Let $r, z$ and each of $u_i$ be a length $n$ vector. I’d like to maximize the correlation between $z$ and $r$ (when that correlation is positive) while keeping $z$ “away” from $u_i$’s. Formally, \...
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8 votes
2 answers
216 views

Convex Optimization: Separation of Cones

I am trying to solve Exercise 2.39 at Boyd and Vandenberghe's Convex Optimization book. In one source, the answer is given as: 2.39 Separation of cones. Let $K$ and $\tilde K$ be two convex cones ...
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8 votes
1 answer
232 views

Distributed optimization problem

Consider the following optimization problem: \begin{equation} \label{eq:1} \min_{x\in\mathcal X} \max_{i\in\mathcal I}\sum_{j\in\mathcal J} f_i(x_{(j)}), \end{equation} where $\mathcal{I}$ and $\...
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7 votes
5 answers
755 views

Fast way to repeatedly solve many similar LPs/QPs in parallel

I am running a simulation where I need to repeatedly solve a set of LPs or QPs with slightly different input parameters for a Model Predictive Control application. The problem is I need it to be fast, ...
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2 answers
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Difference between exploration and exploitation in Simulated Annealing algorithm

In evolutionary algorithms, two main abilities maintained which are Exploration and Exploitation. In Exploration the algorithm searching for new solutions in new regions, while Exploitation means ...
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Is $\min \ x^3 \ \mathrm{s.t.}\ x \geq 0$ a convex problem?

The problem $$\min \ x^3 \ \mathrm{s.t.} \ x \geq 0$$ is sometimes said to be a convex optimization problem. $f(x) = x^3$ is not a convex function. However, in the domain of $x\geq 0$ it is convex. So ...
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7 votes
1 answer
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Ridge Regression lagrange duality

In every machine learning book we see that it is roughly mentioned that the ridge regression: $$p_1^* = \min\limits_{\beta} \ \left( \mathrm{RSS} + \lambda\sum_{j=1}^p \beta_j^2 \right)$$ is ...
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6 votes
3 answers
429 views

Are there any parallel methods for solving multiple general nonlinear convex optimization problems?

I want to find a parallel computing method for general nonlinear convex optimization problems with constraints. A parallel method that can solve a bundle of nonlinear convex problems simultaneously, ...
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6 votes
2 answers
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Find a point inside non-empty difference of ellipsoids

Given two ellipsoids \begin{align}\mathcal{E}_1 &= \{ X \mid X^\top A_1 X + 2B_1^\top X + C_1 \leq 0\}\\\mathcal{E}_2 &= \{ X \mid X^\top A_2 X + 2 B_2^\top X + C_2 \leq 0\}\end{align} are ...
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6 votes
1 answer
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Convexity of the variance of a mixture distribution

$X$ is a random variable that is sampled from the mixture of uniform distributions. In other words: $$X \sim \sum_{i=1}^N w_i \cdot \mathbb{U}(x_i, x_{i+1}),$$ where $\mathbb{U}(x_i, x_{i+1})$ denotes ...
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6 votes
2 answers
408 views

Existence of Optimal Solution

Assume we are solving $\min\{f(x) \ | \ x \in S \}$. If $f: \mathbb{R}^n \mapsto \mathbb{R}$ is a proper closed convex function, and $S$ is a non-empty closed convex set, does this imply that the ...
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6 votes
0 answers
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Is this a valid strong polynomial algorithm for deciding LP feasibility?

Let $$A \cdot X + B \preceq 0$$ be a system of linear inequalities with $X \in \mathbb{R}^n$ $A\in \mathbb{R}^{m\times n}$ and $B \in \mathbb{R}^m$ where $m \geq n$. According to Farkas lemma, exactly ...
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6 votes
0 answers
53 views

Semi-definite Programming, non standard notation

The usual way to define a semi-definite program (SDP), e.g., as given in Boyd and Vandenberghe's convex optimization book, is: $$ \begin{array}{cl} \min & c^\top x \\ \mathrm{s.t.} & 0 \succeq ...
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5 votes
1 answer
302 views

Solving Quadratically Constrained Quadratic Program with Cross Product Terms Only

I'm totally new to the world of optimization and I have an optimization problem that I think it can be formulated as Mixed Integer Quadratically Constrained Quadratic Program (QCQP) but I'm not sure ...
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5 votes
3 answers
129 views

What is the go-to practical method for optimizing separable quadratic programs?

I have a quadratic program that looks like this: For fixed vector $b$, and matrices $A_1, ..., A_n$, Find column vectors $x_1, ..., x_n$ that minimize $\sum_{i=1}^n x_i ^T 1 1^T x_i$ subject to $\sum_{...
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5 votes
2 answers
126 views

Black-box optimization of a single parameter function with high cost evaluation

I need to solve a series of single parameter black-box minimization problem. The underlying cost functions are quite simple. They always have the same shape: a global minimum inside a fixed interval (-...
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  • 175
5 votes
1 answer
138 views

Constraints like "max(column a + column b) == 2" are not DCP

I am struggling with the following constraint on a minimization problem cvx.max(z[:, i] + z[:, j]) == 2 where z is a Boolean ...
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  • 585
5 votes
1 answer
336 views

What is a good way to penalise LP relaxation?

I have a binary integer program. It is of a large size and the solver is taking longer time. I am thinking of relaxing the binary integer variable and making it a continuous variable. How can I ...
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5 votes
2 answers
213 views

Piecewise linear and global optimization

I am new to OR, and apologies if my mathematical notation is not clear. I have tried my best to keep it concise and given an explanation with numerical data. I would like to understand: Can this ...
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  • 81
5 votes
1 answer
157 views

Which solver solves PSD constrained convex non-linear problem

I have a problem with a vector variable $w \in \mathbb{R}^n$ and a symmetric matrix variable $V \in \mathbb{R^{n \times n}}$. I am solving a problem which is roughly like: \begin{align} \begin{array}{...
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5 votes
1 answer
165 views

Practical open source LP solvers for large linear programming problem with $10^7$ parameters

I have an LP problem of the form $\min\ c^Tx$ subject to $Ax\leq b$ where $x$ consists of 30 million parameters and $A$ is a very very sparse matrix of size 30M by 30M (with only 3 ones per row). I ...
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  • 151
5 votes
1 answer
168 views

Prove that $x^*$ is an optimal solution where $f_0$ is $C^1$ and convex and $f_i$ are $C^1$ and strictly convex functions

Let $x^*$ be a feasible solution of the following convex optimization problem \begin{align}\min&\quad f_0(x)\\\text{s.t.}&\quad f_i(x)\leq0,i=1,\ldots,m\end{align} where $f_0$ is $C^1$ and ...
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  • 395
5 votes
2 answers
94 views

Are "polynomial-time" algorithms for convex minimization actually pseudopolynomial time and/or FPTASes?

Motivating example This question concerns continuous convex minimization. However, the motivating example is the classic binary knapsack problem $$\text{maximize}\quad v^T x \qquad \text{subject to}\...
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  • 374
5 votes
1 answer
63 views

Enforce specific mean and standard deviation on data

Suppose I have some dataset $X = \{x_1, x_2, \ldots, x_n\}$ which has a mean $\bar{X}$ and a standard deviation $\sigma_X$. Now, suppose that I want to trim the tails of the dataset such that the new ...
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  • 193
5 votes
0 answers
92 views

Polyhedra to Simplex by using convex combination of vertices

Optimization problems over linear constraints (defining a convex polyhedron) can be written as optimization over a simplex in a higher dimension. Let $\mathcal{P}$ be a bounded polyhedron, and the ...
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5 votes
0 answers
837 views

Convexity of the projection of a convex set

Question: A set $S \subset \mathbb{R}^m \times \mathbb{R}^n$ is convex. Using the fact that affine maps preserves convexity prove that $S(y) = \{x \in \mathbb{R}^m\mid (x,y)\in S \}$ and $\hat{S} = ...
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4 votes
1 answer
285 views

Why does some solvers can only solve conic optimization problems?

Famous solvers like sedumi, sdpt3, mosek can solve conic optimization, but not more general convex optimization. Why? I know many convex problems can be formulated as conic, but still confused.
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4 votes
2 answers
215 views

How to solve this convex problem heuristically?

I have the following problem $$\max_{X_{i,j},i\in N_{U},j\in N_{B}}\sum_{i=1}^{N_U}\sum_{j=1}^{N_B}R_{i,j}X_{i,j}$$ $$\text{subject to}$$ $$a_{\min}\le\sum_{j=1}^{N_B}X_{i,j}\le a_{\max}, \forall i$$ $...
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4 votes
1 answer
108 views

How to check for convexity of the inequality constraint $−x^2+y−1\ge0$ for a minimization objective function?

I checked the Hessian which is $\begin{bmatrix}-2&0\\0&0\end{bmatrix}$ which is negative semidefinite but according to the sketch of the function it is convex. What am I missing?
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  • 125
4 votes
2 answers
160 views

Eliminating Variables in Semidefinite Programs Using Equality Constraints

Suppose I have an SDP \begin{align}\min_{X \in \mathbb{S}^{n}_{+}}&\quad f(X)\\\text{s.t.} &\quad X_{i,j} = c_{i,j} \quad \forall (i,j) \in I,\end{align} where $I \subseteq [n] \times [n]$ and ...
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4 votes
1 answer
215 views

Inverse of weighted sum of positive definite matrices

Let us suppose $I_1, \ldots, I_n$ are symmetric and positive definite matrices. Let $\mathbf{u}$ be the vector with $n$ 1s. I'm interested in the following optimization problem: $$\min \; u^T (x_1I_1+...
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  • 151
4 votes
2 answers
259 views

Any Solution for $k$-means with minimum and maximum cluster size constraint?

I am looking for an efficient approach to $k$-means clustering with minimum cluster size constraints. The clusters are non overlapping, so, one point can belong to only one cluster. $N$ be the number ...
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4 votes
1 answer
148 views

large scale optimization with Python

I am dealing with the following optimization problem: $$ \underset{x}{\min} q(x) $$ subject to $$ l_{x} \leq x \leq u_{x} \,\,\,\, \text{ and } \,\,\,\, l_{a} \leq Ax \leq u_{a}. $$ where $q(x)$ is a ...
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  • 43
4 votes
3 answers
121 views

How to find the point on the exterior of a given set of points?

Suppose we do have a set of points (all on a plane ). How to find the smallest hull containing all these points ? How to find the points (among these given points) that are at the exterior layers of ...
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4 votes
1 answer
97 views

Is it always possible to optimize a multivariate function sequentially?

Suppose we have a multivariate function like $f(x,y,z)$ which should be maximized with the constraints $g_i(x,y,z)\le 0 \quad \forall i$. The general rule is to use KKT conditions and derive all KKT ...
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  • 2,063
4 votes
1 answer
160 views

Cutting-planes application procedure for a specific problem

Sort of following up with this question. I reformulated another model to make it convex and possibly solve it with some cut generation method. I would like to double-check whether I am doing it ...
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  • 1,229
4 votes
1 answer
146 views

Maximization of a nonconvex bi-variate function

Suppose we have a bi-variate function like $f(x,y)$ which is concave in $x$, $\frac{d^2f(x,y)}{dx^2} = -g(x,y)<0$ (that is $f(x,y)$ can be a function with high order in $x$ ) but convex in $y$, ...
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  • 2,063
4 votes
1 answer
253 views

Conditions required for strong duality to hold for SDPs

According to Wikipedia, strong duality holds when "the primal optimal objective and the dual optimal objective are equal." What are the necessary conditions for strong duality to hold in ...
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  • 173
4 votes
1 answer
178 views

Minimize a convex function over a sphere

Problem description Let $\mathcal{C} = \{X \in \mathbb{R}^n \mid g(X) \leq 0\}$ with $g(X)$ a convex function. Suppose I need to solve the feasibility problem, for a given $r>0$ $$ \exists ^?X \...
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  • 487
4 votes
1 answer
121 views

DCP formulation of sum of nonconvex and convex functions

I am trying to find a DCP formulation for the following convex objective function (using CVXPY): Let $x$ be the $N$-dimensional vector variable on which we optimize on, $c$ be a known scalar value ...
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  • 41
4 votes
1 answer
80 views

How to prove pseudo-convexity of a discrete function?

Given a general function $f:\Bbb Z\to\Bbb R$ is there a simple way to verify whether $f(x)$ is pseudo-convex or not?
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  • 43
4 votes
1 answer
100 views

Optimization of strongly convex functions with approximate evaluations of gradient and Hessian

Suppose I want to find the minimum of a differentiable, strongly convex function $f:\mathbb{R}^n\to\mathbb{R}$ with constant $\mu>0$. That is, for all $x,y\in\mathbb{R}^n$, I have that: $$f(y) \geq ...
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4 votes
1 answer
172 views

Can we get the closed-form solution for this problem?

Can we get the closed-form solution for this problem? \begin{align} \min&\quad\sum_{i=1}^N\frac{K_i}{x_i\log_2(1+\frac{Q_i}{x_i})}\\ {\rm{s.t.}}&\quad\sum_{i=1}^N x_i\le X \end{align} wherein $...
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4 votes
0 answers
105 views

Weighted nuclear norm minimization

The problem. Let $X,A \in\mathbb{R}^{n\times m}$ and let $W\in\mathbb{R}^{nm\times nm}$ be a positive definite matrix. I want to know if there is a closed-form solution to this problem $$ \min_{X} \...
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4 votes
0 answers
95 views

Strong Duality and Slater Condition

I am studying the Duality Chapter of Convex Optimization by Boyd. Is it possible that strong duality holds for non-convex optimization? If yes, is there any specific condition? And, what is the ...
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  • 2,063
3 votes
2 answers
968 views

How can I express this max-min in CPLEX?

Initially, I had the below objective function $\max \sum_{u=1}^{U}\sum_{c=1}^{C}x_{u,c}d_{u,c}$ where $x_{u,c}$ are optimization variables I modelled this in CPLEX as ...
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