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Questions tagged [convex-optimization]

Convex minimization, 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.

Applications:

  • automatic control systems
  • estimation and signal processing
  • communications and networks,
  • electronic circuit design
  • data analysis and modeling
  • statistics
  • finance
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Handling rounding errors of exponential function in convex optimization for scheduling web crawler

I am writing web crawler scheduler and have run into problems. First I will describe how I'm trying to find optimal schedule for when my crawler is visiting the page and then I will present my problem. Scheduler definition Scheduler is based on this…
riogrande
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How to use cvxopt with DSDP?

I'm trying to use DSDP (semidefinite programming package) with cvxopt. I have both of them installed (matlab version for DSDP). I have Python 2.5.2. When trying to use dsp(..., solver='dsdp') I get an errors mentioning solvers.dsdp is not…
R S
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How do I choose the package to solve this convex optimization in Python?

My problem is defined as below, minΣ(||xi-Xci||^2+ λ||ci||), s.t cii = 0, where X is a matrix of shape d * n and C is of the shape n * n, xi and ci means a column of X and C separately. X is known here and based on X we want to find C. I got…
xxx222
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Linear program with arbitrary constraints in Spark?

I have a problem that can be formulated as a convex optimization problem with a linear objective function and linear equality and inequality constraints, but with an enormous number of parameters. I can solve this problem in reasonable time on a…
goodepic
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Minimizing quadratic function subject to norm inequality constraint

I am trying to solve the following inequality constraint: Given time-series data for N stocks, I am trying to construct a portfolio weight vector to minimize the variance of the returns. the objective function: min w^{T}\sum w s.t.…
user3742038
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How do i check if a cost function is Concave or Convex?

How do i check if this cost function is concave or convex? I also want to find if this has a single or multiple minimums. Effort Made; function [w,pi,costvalue] = main_cost(inputdata, tmax, alpha_ini,somrow,somcol) %main cost function; To…
Young_DataAnalyst
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How to interpret the result of cvxopt.solvers.qp?

There is no enough documentation and my math knowledge is limited. The model sol = solvers.qp(P=P, q=q,G=G,h=h, A=L, b=t) pcost dcost gap pres dres 0: 6.3316e+08 6.3316e+08 7e+00 3e+00 7e-10 1: 6.3316e+08 6.3316e+08 …
Hello lad
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Dynamic convex hull trick

I was reading about interesting algorithms in my free time and I just discovered the convex hull trick algorithm, with which we can compute the maxima of several lines in the plane on a given x coordinate. I found this article: …
porcupine
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Convex programming with CVXOPT or CVXPY

I need to solve an optimization problem with CVXOPT or CVXPY in Python and I have run into difficulties. The objective function is Minimize Sum(a*x^2+b/x) subject to the following constraints 5 <= x < 40; sum(v/d)<=T where vector x is the…
Vitor Lemos
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Kernel Matching Pursuit on CVXPY

I am writing code for Kernel Matching pursuit.In that, I have used to cvxpy for convex optimization.I have to minimize following objective which is based on this paper:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6815769 and code is as…
Gunjan naik
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Issues in Convergence of Sequential minimal optimization for SVM

I have been working on Support Vector Machine for about 2 months now. I have coded SVM myself and for the optimization problem of SVM, I have used Sequential Minimal Optimization(SMO) by Dr. John Platt. Right now I am in the phase where I am going…
Amol Joshi
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How do I find out when a function is strongly convex?

My professor said( i might have misunderstood) That if I have a function,and i take the second derivative,and somehow I get 0,hen it is not strongly comvex! Example x^2 strongly comvex because its second derivative = 2 example x^4 not strongly…
LoveMeow
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R linear programming set up in linprog package ignores constraint (less than or equal to) using solveLP

I am using solveLP in the linprog R package to solve a simple linear programming problem: minimize -x1-x2 subject to 2*x1+x2+x3 =12 x1+2*x2 +x4 = 9 x1,x2,x3,x4 >=0 which has dual equivalent: maximize 12*y1+9*y2 subject…
crogg01
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Alternatives to FMINCON

Are there any faster and more efficient solvers other than fmincon? I'm using fmincon for a specific problem and I run out of memory for modest sized vector variable. I don't have any supercomputers or cloud computing options at my disposal, either.…
sprajagopal
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Nesterovs third method - implementation in python

I am looking at implementing Nesterov's method for my algorithm being written in python. Can anyone please point me to docs which can help me get started in terms of implementation of this method? I am a programmer by profession and hence looking…
Ananth
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