Iteratively Re-weighted Least Squares for Sums of Convex Functions

Transcription

1 Iteratively Re-weighted Least Squares for Sums of Convex Functions James Burke University of Washington Jiashan Wang LinkedIn Frank Curtis Lehigh University Hao Wang Shanghai Tech University Daiwei He University of Washington Iteratively Re-weighted Least Squares / 3

2 Outline Classical Iterative Re-Weighting Exact Penalization 3 The Iterative Re-Weighting Algorithm (IRWA) 4 Convergence 5 A Numerical Experiment: l -SVM 6 Least-Squares and Sums of Convex Functions 7 General Least-Squares Based Algorithms 8 General Methods Applied to J(x, ε) = l dist (A i x + b i C i ) + ɛ i 9 Comparison with IRWA Iteratively Re-weighted Least Squares / 3

3 Classical Iterative Re-Weighting: A mainstay of statistical computing l -Regression where A i is the ith row of A. m min Ax + b x R n := A i x + b i, Iteratively Re-weighted Least Squares 3 / 3

4 Classical Iterative Re-Weighting: A mainstay of statistical computing l -Regression where A i is the ith row of A. Iterative Least Squares Approach Having x k approximate Ax + b by m min Ax + b x R n := A i x + b i, Ax + b = m A i x + b i = m A i x + b i A i x + b i m A i x + b i A i x k + b i. Solve the linear least squares problem in the approximation for x k+ and iterate. Iteratively Re-weighted Least Squares 3 / 3

5 Classical Iterative Re-Weighting: A mainstay of statistical computing l -Regression where A i is the ith row of A. Iterative Least Squares Approach Having x k approximate Ax + b by m min Ax + b x R n := A i x + b i, Ax + b = m A i x + b i = m A i x + b i A i x + b i m A i x + b i A i x k + b i. Solve the linear least squares problem in the approximation for x k+ and iterate. Modified ɛ-approximate Weighted Least Squares m Ax + b w(x k, ɛ k i ) A i x + b i where w(x k, ɛ k i ) := / A i x k + b i + (ɛ k i ). Iteratively Re-weighted Least Squares 3 / 3

6 Classical Iterative Re-Weighting Lawson 96 Rice and Usow 968 Karlovitz 970 Kahng 97 Fletcher, Grant and Hebden 97 Schlossmacker 973 Beaton and Tukey 974 Wolke and Schwetlick 988 O Leary 990 Burrus and Burreto 99 Vargas and Burrus 999 Iteratively Re-weighted Least Squares 4 / 3

7 The Exact Penalty Subproblem min J 0(x) := g T x + x X x T Hx + dist (A i x + b i C i ), g R n, H S+, n A R m i n, b R m i, and C i R m i are non-empty closed convex, and dist (y i C i ) := inf y i z i = y i P Ci (y i ) z i C i P Ci is the projection onto C i. Examples: Equality: C i := {0} Inequality: C i := (, 0] Inequality: C i = [l i, u i ] l l -ball: y i l τ i, l =,, Trust region: X = {x x l τ}, l =,, Iteratively Re-weighted Least Squares 5 / 3

8 NLP and Exact Penalties Nonlinear Programming (NLP): minimize f (x) subject to F (x) C and x X - f : R n R and F : R n R m smooth - X R n and C R m non-empty, closed, and convex - C := C C C l, F (x) := (F (x) F (x)... F l (x)) F i (x) C i, F i : R n R m i, C i R m i Exact penalty formulation: Given α > 0 min f (x) + α x X Local direction finding approximation dist (F i (x) C i ) min J 0(x) := g T x + x X x T Hx + dist (A i x + b i C i ), Iteratively Re-weighted Least Squares 6 / 3

9 ɛ-approximate Re-Weighted Least Squares Let P Ci (y) C i be the projection of y onto C i, i.e. y P Ci (y) = dist (y C i ). At (x k, ɛ k ) approximate dist (A i x + b i C i ) = A i x + b i P Ci (A i x + b i ) by dist (A i x + b i C i ) dist (A i x + b i C i ) dist (A i x k + b i C i ) + (ɛ k i ) Iteratively Re-weighted Least Squares 7 / 3

10 ɛ-approximate Re-Weighted Least Squares Let P Ci (y) C i be the projection of y onto C i, i.e. y P Ci (y) = dist (y C i ). At (x k, ɛ k ) approximate dist (A i x + b i C i ) = A i x + b i P Ci (A i x + b i ) by dist (A i x + b i C i ) dist (A i x + b i C i ) dist (A i x k + b i C i ) + (ɛ k i ) = A ix + b i P Ci (A i x + b i ), dist (A i x k + b i C i ) + (ɛ ki ) Iteratively Re-weighted Least Squares 7 / 3

11 ɛ-approximate Re-Weighted Least Squares Let P Ci (y) C i be the projection of y onto C i, i.e. y P Ci (y) = dist (y C i ). At (x k, ɛ k ) approximate dist (A i x + b i C i ) = A i x + b i P Ci (A i x + b i ) by dist (A i x + b i C i ) dist (A i x + b i C i ) dist (A i x k + b i C i ) + (ɛ k i ) = A ix + b i P Ci (A i x + b i ), dist (A i x k + b i C i ) + (ɛ ki ) A i x + b i P Ci (A i x k + b i ) dist (A i x k + b i C i ) + (ɛ k i ), Iteratively Re-weighted Least Squares 7 / 3

12 The full approximation J 0 (x) = g T x + x T Hx + dist (A i x + b i C i ) g T x + x T Hx + w i (x k, ɛ k ) A i x + b i P Ci (A i x k + b i ), where w i (x k, ɛ k ) = / dist (A i x k + b i C i ) + (ɛ k i ). Iteratively Re-weighted Least Squares 8 / 3

13 The full approximation J 0 (x) = g T x + x T Hx + dist (A i x + b i C i ) g T x + x T Hx + w i (x k, ɛ k ) A i x + b i P Ci (A i x k + b i ), where w i (x k, ɛ k ) = / dist (A i x k + b i C i ) + (ɛ k i ). For simplicity, we drop the term g T x + x T Hx from future consideration. This can be done with (almost) no loss in generality. Iteratively Re-weighted Least Squares 8 / 3

14 The Iterative Re-Weighting Algorithm (IRWA) IRWA: x k+ arg min x X l w i(x k, ɛ k ) Ai x + b i P Ci (A i x k + b i ) with ɛ k 0. Iteratively Re-weighted Least Squares 9 / 3

15 The Iterative Re-Weighting Algorithm (IRWA) IRWA: x k+ arg min x X l w i(x k, ɛ k ) Ai x + b i P Ci (A i x k + b i ) with ɛ k 0. Initialize x 0, ɛ 0, M, ν > 0, η, σ, σ (0, ). Solve the re-weighted subproblem for x k+. 3 Set q k i := A i (x k+ x k ) If [ q i k M dist ( Ai x k ) + b i C i + (ɛ k i ) ] +ν, i =,..., l, choose ɛ k+ (0, ηɛ k ]; otherwise, set ɛ k+ := ɛ k. 4 If x k+ x k σ and ɛ k σ, stop; else k := k + and go to Step. Iteratively Re-weighted Least Squares 9 / 3

16 Convergence Let (x 0, ɛ 0 ) X R ++. Suppose that the sequence {(x k, ɛ k )} k=0 is generated by IRWA with σ = σ = 0. Let S := { k ɛ k+ ηɛ k }. If either ker(a) = {0} or X = R n, then any cluster point x of the subsequence {x k } k S satisfies 0 J 0 ( x) + N ( x X ). If it is assumed that ker(a) = {0}, then x k x the unique global solution, and in at most O(/ε ) iterations, x k is an ε-optimal solution, i.e. J 0 (x k ) inf x X J 0(x) + ε. Iteratively Re-weighted Least Squares 0 / 3

17 Support Vector Machine Experiment Consider the exact penalty form of the l -SVM problem: m n y i x ij β j + λ β, x i min β R n j= where {(xx i, y i )} m are the training data points with x i R n and y i {, } for each i =,..., m, and λ is the penalty parameter. + Iteratively Re-weighted Least Squares / 3

18 Support Vector Machine Experiment Consider the exact penalty form of the l -SVM problem: m n y i x ij β j + λ β, x i min β R n j= where {(xx i, y i )} m are the training data points with x i R n and y i {, } for each i =,..., m, and λ is the penalty parameter. For purposes of numerical comparison, we randomly generate 40 problems where X ranges from a matrix to a matrix where the sparsity of the true solution is always 0% of the number of columns. We compare the performance of IRWA with an ADMM implementation whose parameters are pre-optimized for performance on this data set. The least-squares subproblems for both methods are solved using the same CG solver. The effort is measured in terms of the number of CG solves. + Iteratively Re-weighted Least Squares / 3

19 ADAL - IRWA Numerical Comparison: Number of CGs With Nesterov acceleration. ADAL IRWA Objective CG value Problem Figure : In all 40 problems, IRWA obtains smaller objective function values with fewer CG steps. Iteratively Re-weighted Least Squares / 3

20 ADAL - IRWA Comparison: Number of False Positives ADAL IRWA err e 05 e e 05 e Figure : For both thresholds 0 4 and 0 5, IRWA yields fewer false positives in terms of the numbers of zero values computed. The numbers of false positives is similar for the threshold 0 3. At the threshold 0 5, the difference in recovery is dramatic with IRWA always having fewer than 4 false positives while ADAL has a median of about 000 false positives. Iteratively Re-weighted Least Squares 3 / 3

21 Least-Squares and Sums of Convex Functions Goals min x φ(x) := f (Ax + b) = f i (A i x + b i ) Minimize φ based on properties of the individual f i only with minimal linkage between the f i s (splitting). For example, apply Prox to the individual f i s, but not to the function φ as a whole. The A i s only enter through weighted least-squares: min x w i (x, p) A i x + b i s i (p), where p E 0 is a parameter vector and s : E 0 W. Iteratively Re-weighted Least Squares 4 / 3

22 Least-Squares and Sums of Convex Functions Goals min x φ(x) := f (Ax + b) = f i (A i x + b i ) Minimize φ based on properties of the individual f i only with minimal linkage between the f i s (splitting). For example, apply Prox to the individual f i s, but not to the function φ as a whole. The A i s only enter through weighted least-squares: min x w i (x, p) A i x + b i s i (p), where p E 0 is a parameter vector and s : E 0 W. The procedures we consider are iterative with x c and y c := Ax c + b representing the current best approximate solutions, and N(c) is the number of iterations to obtain x c. Iteratively Re-weighted Least Squares 4 / 3

23 Separate the Roles of the f i s and the A i s min x φ(x) := f (Ax + b) = f i (A i x + b i ) min x f i (y i ) s.t. y b + Ran A Iteratively Re-weighted Least Squares 5 / 3

24 Separate the Roles of the f i s and the A i s min x φ(x) := f (Ax + b) = f i (A i x + b i ) min x f i (y i ) s.t. y b + Ran A [ Prox γi,f i y := argmin z f i (z) + ] z y γ i Iteratively Re-weighted Least Squares 5 / 3

25 Least-Squares Based Algorithms Projected Subgradient: min x A i (x x c ) + t c i g c i, g c i f i (yi c ), ti c R := L N(c), O(/ɛ ) no acc Iteratively Re-weighted Least Squares 6 / 3

26 Least-Squares Based Algorithms Projected Subgradient: min x A i (x x c ) + t c i g c i, Projected Prox-Gradient: g c i f i (yi c ), ti c R := L N(c), O(/ɛ ) no acc min x A i (x x c ) + ti c (I Prox γi,f i )(A i x c + b i ), tc j := γ j l γ i, O(/ɛ) acc Iteratively Re-weighted Least Squares 6 / 3

27 Least-Squares Based Algorithms Projected Subgradient: min x A i (x x c ) + t c i g c i, Projected Prox-Gradient: g c i f i (yi c ), ti c R := L N(c), O(/ɛ ) no acc min x ADAL: min x A i (x x c ) + ti c (I Prox γi,f i )(A i x c + b i ), tc j := γ j l γ i, O(/ɛ) acc (A i (x x c ) + (I Prox γi,f γ i )(A i x c + b i + γ i ui c ), O(/ɛ) acc i u + i := u c i + γ i (A i x + + b i Prox γi,f i (A i x c + b i + γu c i )). Iteratively Re-weighted Least Squares 6 / 3

28 Application to J(x, ε) = l dist (A i x + b i C i ) + ɛ i A i (x x c ) + ti c (I P Ci )(A i x c + b i ) Projected Subgradient for J(x, 0) dist(x 0 Σ ) ti c :=, if A i x c + b i / C i, ldist(a i x c +b i C i ) N(c) 0, otherwise. Projected gradient for J(x, ε) t c j := ɛ min dist (A j x c + b j C j ) + ɛ j Projected Prox-Gradient for J(x, 0) γ j l, dist γ (A j x c + b j C j ) γ j tj c i :=, dist (A j x c + b j C j ) > γ j dist (A j x c +b j C j ) l γ i (Σ solution set) ADAL for J(x, 0) Same as projected prox-gradient but t c j := and we include shifts γ i u c i. Iteratively Re-weighted Least Squares 7 / 3

29 Comparison with IRWA General Methods: Ai (x x k ) + ti c (I P Ci )(A i x k + b i ) O( ɛ ) acc IRWA: Ai (x x k ) + (I P Ci )(A i x k + b i ) dist (A i x k + b i C i ) + ɛ O( ) no acc ɛ i Iteratively Re-weighted Least Squares 8 / 3

30 Comparison with IRWA General Methods: Ai (x x k ) + ti c (I P Ci )(A i x k + b i ) O( ɛ ) acc IRWA: Ai (x x k ) + (I P Ci )(A i x k + b i ) dist (A i x k + b i C i ) + ɛ O( ) no acc ɛ i New Improved IRWA: ɛ i A i(x x k ) + O( ɛ )!! acc ɛ i dist (A i x k + b i C i ) + ɛ i (I P Ci )(A i x k + b i ) Iteratively Re-weighted Least Squares 8 / 3

31 Comparison of Projected Gradient and New IRWA Projected gradient for J(x, ε) A i(x x k ) + ɛ min dist (A i x k + b i C i ) + ɛ i (I P Ci )(A i x k + b i ) New Improved IRWA: ɛ i A i(x x k ) + ɛ i dist (A i x k + b i C i ) + ɛ i (I P Ci )(A i x k + b i ) Iteratively Re-weighted Least Squares 9 / 3

32 Comparison of Projected Gradient and New IRWA Projected gradient for J(x, ε) A i(x x k ) + ɛ min dist (A i x k + b i C i ) + ɛ i (I P Ci )(A i x k + b i ) New Improved IRWA: ɛ i A i(x x k ) + ɛ i dist (A i x k + b i C i ) + ɛ i (I P Ci )(A i x k + b i ) When all ɛ i have the same value ɛ, they are the same algorithm! Iteratively Re-weighted Least Squares 9 / 3

33 Unaccelerated versions with ɛ = 0.0. Figure : In all 0 problems, the old IRWA with iterative re-weighting obtains similar objective function values with fewer CG steps. Iteratively Re-weighted Least Squares 0 / 3

34 Accelerated versions with ɛ = 0.0. Figure : In all 0 problems, the old IRWA with iterative re-weighting obtains similar objective function values with many fewer CG steps. Iteratively Re-weighted Least Squares / 3

35 Reference Thank you! Iteratively Reweighted Linear Least Squares for Exact Penalty Subproblems on Product Sets with F. Curtis, H. Wang and J. Wang. SIAM J. Optim. 5(05): Iteratively Re-weighted Least Squares / 3

36 The Euclidean Huber Distance to a Convex Set C E be non-empty closed and convex h := dist ( C ) the Euclidean distance to C The Euclidean Huber distance to C is just the Moreau-Yosida envelope of the distance to C. e γ h(y) = { γ dist (y C ), if dist (y C ) γ, dist (y C ) γ, if dist (y C ) > γ, { PC (y), if dist (y C ) γ, Prox γ,h (y) = γ y dist (y C ) (y P C (y)), if dist (y C ) γ. Iteratively Re-weighted Least Squares 3 / 3