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Iteration-complexity of first-order penalty methods for convex programming

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Abstract

This paper considers a special but broad class of convex programming problems whose feasible region is a simple compact convex set intersected with the inverse image of a closed convex cone under an affine transformation. It studies the computational complexity of quadratic penalty based methods for solving the above class of problems. An iteration of these methods, which is simply an iteration of Nesterov’s optimal method (or one of its variants) for approximately solving a smooth penalization subproblem, consists of one or two projections onto the simple convex set. Iteration-complexity bounds expressed in terms of the latter type of iterations are derived for two quadratic penalty based variants, namely: one which applies the quadratic penalty method directly to the original problem and another one which applies the latter method to a perturbation of the original problem obtained by adding a small quadratic term to its objective function.

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References

  1. Auslender, A., Teboulle, M.: Interior gradient and proximal methods for convex and conic optimization. SIAM J. Optim. 16, 697–725 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  2. Lan, G., Lu, Z., Monteiro, R.D.C.: Primal–dual first-order methods with \(\cal O(1/\epsilon )\) iteration-complexity for cone programming. Math. Program. 126, 1–29 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  3. Monteiro, R.D.C., Svaiter, B.F.: Complexity of variants of tsengs modified f-b splitting and korpelevichs methods for hemi-variational inequalities with applications to saddle-point and convex optimization problems. Manuscript, School of ISyE, Georgia Tech, Atlanta, June (2010)

  4. Monteiro, R.D.C., Svaiter, B.F.: On the complexity of the hybrid proximal projection method for the iterates and the ergodic mean. SIAM J. Optim. 20, 2755–2787 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Nemirovski, A.: Prox-method with rate of convergence \(o(1/t)\) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems. SIAM J. Optim. 15, 229–251 (2005)

    Article  MathSciNet  Google Scholar 

  6. Nesterov, Y.E.: A method for unconstrained convex minimization problem with the rate of convergence \(O(1/k^2)\). Doklady AN SSSR 269, 543–547 (1983)

    MathSciNet  Google Scholar 

  7. Nesterov, Y.E.: Introductory Lectures on Convex Optimization: A Basic Course. Kluwer, Massachusetts (2004)

    MATH  Google Scholar 

  8. Nesterov, Y.E.: Smooth minimization of nonsmooth functions. Math. Program. 103, 127–152 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Nesterov, Y.E.: Dual extrapolation and its applications to solving variational inequalities and related problems. Math. Program. 109, 319–344 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Tseng, P.: On Accelerated Proximal Gradient Methods for Convex-Concave Optimization. Manuscript, University of Washington, Seattle, May (2008)

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Correspondence to Guanghui Lan.

Additional information

The work of the first author was partially supported by NSF Grants CCF-0430644 and CMMI-1000347. The work of the second author was partially supported by NSF Grants CCF-0430644, CCF-0808863 and CMMI-0900094 and ONR Grants N00014-08-1-0033 and N00014-11-1-0062.

Appendix

Appendix

In this section, we prove Proposition 1.

Proof of Proposition 1

Define \(\mathcal{C}:= \{(v, t) \in \mathfrak R ^m \times \mathsf{I\!\!R}: \Vert v\Vert \le t\}\) and let \(\mathcal{C}^*\) denote the dual cone of \(\mathcal{C}\). It is easy to see that \(\mathcal{C}^* = \{(\tilde{v}, \tilde{t}) \in \mathfrak R ^m \times \mathsf{I\!\!R}: \Vert \tilde{v}\Vert \le \tilde{t}\}\). By definition of \(d_\mathcal{K ^*}\) and conic duality, we have

$$\begin{aligned} d_{{\mathcal{K}^*}}(u)&= \inf _{\tilde{k}, \tilde{t}} \{\tilde{t}: \Vert u-\tilde{k}\Vert \le \tilde{t}, \, \, \tilde{k} \in {\mathcal{K}^*}\}\\&= \inf _{\tilde{k}, \tilde{t}, \tilde{v}} \{\tilde{t}: \tilde{v} + \tilde{k} = u, \,\, (\tilde{v}, \tilde{t}) \in \mathcal{C}^*, \,\, \tilde{k} \in {\mathcal{K}^*}\} \\&= \sup _{(v,k,t)} \{ \langle u, y \rangle : t = 1, \, y + v = 0, \, y + k = 0, \, (v,t) \in \mathcal{C}, \, k \in \mathcal K \} \\&= \sup \{ \langle u, y \rangle : (-y , 1) \in \mathcal{C}, \, \, - y \in \mathcal K \} \ = \ \sup \{ \langle u, y \rangle : y \in (- \mathcal K ) \cap B(0,1) \}. \end{aligned}$$

Statement (a) follows from the above identity and the definition of the support function of a set (see Sect. 1.1).

To show statement (b), let \(u \in \mathfrak R ^m\) and \(\lambda \in \mathcal K \) be given and assume without loss of generality that \(\lambda \ne 0\). Now noting that \(-\lambda /\Vert \lambda \Vert \in C := (-\mathcal K ) \cap B(0,1)\), we conclude from the above identity that \(d_\mathcal K (u) \ge \langle u, -\lambda /\Vert \lambda \Vert \rangle \), or equivalently, \(\langle u, \lambda \rangle \ge - \Vert \lambda \Vert \, d_\mathcal K (u)\). \(\square \)

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Lan, G., Monteiro, R.D.C. Iteration-complexity of first-order penalty methods for convex programming. Math. Program. 138, 115–139 (2013). https://doi.org/10.1007/s10107-012-0588-x

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