We consider unconstrained randomized optimization of convex objective functions. We analyze the Random Pursuit algorithm, which iteratively computes an approximate solution to the optimization problem by repeated optimization over a randomly chosen one-dimensional subspace. This randomized method only uses zeroth-order information about the objective function and does not need any problem-specific parametrization. We prove convergence and give convergence rates for smooth objectives assuming that the one-dimensional optimization can be solved exactly or approximately by an oracle. A convenient property of Random Pursuit is its invariance under strictly monotone transformations of the objective function. It thus enjoys identical convergence behavior on a wider function class. To support the theoretical results we present extensive numerical performance results of Random Pursuit, two gradient-free algorithms recently proposed by Nesterov, and a classical adaptive step size random search scheme. We also present an accelerated heuristic version of the Random Pursuit algorithm which significantly improves standard Random Pursuit on all numerical benchmark problems. A general comparison of the experimental results reveals that (i) standard Random Pursuit is effective on strongly convex functions with moderate condition number and (ii) the accelerated scheme is comparable to Nesterov's fast gradient method and outperforms adaptive step size strategies.


  1. continuous optimization
  2. convex optimization
  3. randomized algorithm
  4. line search

MSC codes

  1. 90C25
  2. 90C56
  3. 68W20
  4. 62L10

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Information & Authors


Published In

cover image SIAM Journal on Optimization
SIAM Journal on Optimization
Pages: 1284 - 1309
ISSN (online): 1095-7189


Submitted: 1 November 2011
Accepted: 26 March 2013
Published online: 27 June 2013


  1. continuous optimization
  2. convex optimization
  3. randomized algorithm
  4. line search

MSC codes

  1. 90C25
  2. 90C56
  3. 68W20
  4. 62L10



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