Online Optimization Problem-1 (Online machine learning)

1
Institute of Manufacturing Information and Systems (製造資訊與系統研究所)
Institute of Engineering Management (工程管理碩士在職專班)
National Cheng Kung University (國立成功大學)
指導教授：李家岩博士
報告者：洪紹嚴
日期：2016/05/06
Online Optimization Problem(1)

Productivity Optimization Lab Shao-Yen Hung
0. Agenda
1. Problem Definition
2. Background Knowledge
 Convex Function, Subgradient
 Lagrange Multiplier, KKT Conditions
 Loss Function
 Regularization
 BGD(Batch Gradient Descent) vs SGD (Stochastic Gradient Descent)
3. SCR (Simple coefficient Rounding)；TG (Truncated Gradient)
4. FOBOS (Forward-Backward Splitting / Forward Looking Subgradients)
5. RDA (Regularized Dual Averaging)
6. FTRL (Follow-the-Regularized-Leader)
2

0. Agenda
 Loss Function
 Regularization
3

1. Problem Definition (Online Optimization)
• 訓練模型(OLS, NN, SVM……)就是一個最佳化的過程(e.g. 找出最佳
的w*，使預測和實際值之間的誤差總和最小)
• 傳統上，當有新資料進來時，會把訓練過的資料再訓練過一遍，求得
新的w*，這樣的做法稱為Batch Learning(批量學習)。
 缺點：速度慢，記憶體需求大
• 只根據新的資料進行w*的修正，這樣的做法稱為Online Learning。
 優點：速度快，記憶體需求小
• 案例：廣告點擊分析，投資組合管理，推薦商品……(大數據範疇)
4

0. Agenda
 Loss Function
 Regularization
5

 Convex Function
𝜕2 𝑓
𝜕𝑥2 ≥ 0
 Gradient and Subgradient
6
(Gradient)
x
y y=f(x)=|x|
At x=0, subgradient ∂f ∈ [-1, 1]

7
Solve
If f(x) is a convex function：
(1)
(2)
Solve
(Lagrange Multiplier)
(3)
Solve
(KKT Conditions)

最佳化問題描述：
8
• l(W,Z) = 損失函數(loss function)
• Z = 觀測樣本集合
• 𝑋𝑗 = 第j個樣本的特徵向量
• 𝑦𝑗 = h(W, 𝑋𝑗) = 第j個的樣本的預測值
• W = 特徵權重(求解的參數)
損失函數可視為各樣本損失函數的累加：
Linear Regression
Logistic Regression

• Regularization
 avoid overfitting problem
 generate sparsity
9
稱為正則化因子(Regularization)，一個和W有關的函數
convex
convex
(L1, L2 and sparsity)
Lasso
Ridge

Batch Gradient Descent vs Stochastic Gradient Descent ：
10
const
current iteration
全部資料
新資料

0. Agenda
 Loss Function
 Regularization
11

3. SCR (Simple coefficient Rounding)
12
• Solve the problem that L1 Regularization in SGD doesn’t
generate sparsity.
• 3 parameters：
 θ : threshold for deciding whether coefficient is 0 or not
 K : doing truncating after k online steps
 η ： learning rate
𝑊𝑡+1 =
𝑊𝑡 − η
𝜕 𝑙 𝑊𝑡, 𝑍
𝜕𝑊𝑡
, 𝑖𝑓 𝑚𝑜𝑑 𝑡, 𝐾 ≠ 0
𝑇0(𝑊𝑡 − η
𝜕𝑊𝑡
, θ) , 𝑖𝑓 𝑚𝑜𝑑 𝑡, 𝐾 = 0
𝑇0(𝑣𝑖, θ) =
0 , 𝑖𝑓 𝑣𝑖 < θ
𝑣𝑖 , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
We first apply the standard stochastic gradient descent rule, and then round
small coefficients to zero.(Langford et al., 2009)

3. TG (Truncated Gradient)
13
• We observe that the direct rounding to zero is too aggressive. A
less aggressive version is to shrink the coefficient to zero by a
smaller amount. We call this idea truncated gradient.(Langford et
al., 2009)
(Lasso)

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𝑇1(𝑣𝑖, α, θ) =
max 0, 𝑣𝑖 − α , 𝑖𝑓 𝑣𝑖∈ [0, θ]
min 0, 𝑣𝑖 + α , 𝑖𝑓 𝑣𝑖∈ [−θ, 0]
𝑊𝑡+1 = 𝑇1(𝑊𝑡 − η
𝜕 𝑙 𝑊𝑡,𝑍
𝜕𝑊𝑡
, η𝒈𝒊, θ)
𝒈𝒊 =
0 , 𝑖𝑓 𝑚𝑜𝑑 𝑡, 𝐾 ≠ 0
𝐾𝑔 , 𝑖𝑓 𝑚𝑜𝑑 𝑡, 𝐾 = 0
• 4 parameters：
 θ : threshold for deciding whether coefficient is 0 or not
 K : doing truncating after k online steps
 η ： learning rate
 g：gravity parameter

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𝑊𝑡+1 =
𝜕𝑊𝑡
, η𝒈𝒊, θ) , 𝑖𝑓 𝑚𝑜𝑑 𝑡, 𝐾 = 0
𝑊𝑡 − η
𝜕𝑊𝑡
, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Rewrite the TG formulation：
𝑇1(𝑣𝑖, α, θ) =
0 , 𝑖𝑓 |𝑣𝑖| < α
𝑣𝑖 −α ∗ 𝑠𝑔𝑛 𝑣𝑖 , 𝑖𝑓α ≤ 𝑣𝑖 < θ
1) If α = θ SCR(簡單截斷法)
2) If K=1, θ = INF 𝑊𝑡+1 = 𝑇1(𝑊𝑡 − η
𝜕𝑊𝑡
, η𝒈𝒊, 𝑰𝑵𝑭)
= 𝑊𝑡 −η
𝜕𝑊𝑡
− η𝒈𝒊sgn( 𝑊𝑡 −η
𝜕𝑊𝑡
)
L1 Regularization (Lasso)
(if α ≤ |𝑣𝑖|)

3. SCR vs TG vs LASSO
16
α = θ
K=1, θ = INF
−α
α

17

0. Agenda
 Loss Function
 Regularization
18

4. FOBOS (Forward Backward Splitting)
19
(1) 標準梯度下降公式：
(2) L1-FOBOS的梯度下降公式，可以再細分為兩部分：
 前部分：微調發生在梯度下降的結果(𝑾 𝒕+
𝟏
𝟐
)附近
 後部分：處理正則化，產生稀疏性
r(w) = regularization functions
• 事實上，這個方法應該叫FOBAS，可是原作者John Langfold 一開始稱
呼這個方法為FOLOS (Forward Looking Subgradients)，為了避免困擾，
於是把A改成O，變成FOBOS。
(加上L1 regularization的FOBOS)

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(3) 要求得(2)最佳解的充分條件: 0 屬於其subgradient set之中
(4) 因為，(3) 可以改寫成：
(5) 換句話說，把(4)移項之後：
 迭代前的狀態𝑾 𝒕 與梯度
backward
 當次迭代的正則項資訊 𝝏𝒓(𝑾𝒕+𝟏)
forward

21
• L1-FOBOS’s Sparsity
(1)改寫一下原式：
(1)
(2)
λ
r(w) = λ ||w|| 1

22
(2)可以拆解成每一維度權重的總和：
(2)
(3)

23
(3)如果𝒘∗是某一維度𝒘𝒋的最佳解，則𝒘𝒋‧𝒗𝒋 ≥ 0：
(3)
(反證法)
如果上述不成立，表示 𝒘𝒋 ‧ 𝒗𝒋 < 0
因此：
𝟏
𝟐
𝒗 𝟐 <
𝟏
𝟐
𝒗 𝟐 − 𝒘∗‧𝒗 +
𝟏
𝟐
(𝒘∗) 𝟐 <
𝟏
𝟐
𝒗 − 𝒘∗ 𝟐 + λ 𝒘∗
這和𝒘𝒊
∗
為最佳解不符，故𝒘𝒋‧𝒗𝒋 ≥ 0

24
(4)當𝒘𝒋‧𝒗𝒋 ≥ 0：
If 𝐯𝐣 ≥ 0：
a) 𝒘∗ > 0：Since βw*=0, β=0 w* = v - 𝜆
b) 𝒘∗
= 0：Since β≥ 0, 𝑣𝑖 - 𝜆 ≤ 0
That is, w* = max(0, 𝑣𝑖 - 𝜆)
s.t −𝒘𝒋 ≤ 𝟎
]=0 and βw=0
𝜕
𝜕𝑤
[
KKT
𝑤∗
(5) Same as 𝒗𝒋 < 0： w* = -max(0, −𝑣𝑖 - 𝜆)

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(6)綜合(4)(5)的結論：
𝑊𝑖
𝑡+1
= sgn 𝑣𝑖 max(0, |vi| − λ)
𝑊𝑖
𝑡+1
= sgn 𝑊𝑖
𝑡
− η 𝑡 𝑔𝑖
𝑡
max(0, |𝑊𝑖
𝑡
𝑡
| − η 𝑡+
1
2
λ)
𝑣𝑖 = 𝑊𝑖
𝑡
𝑡

26
(7)根據(6)的式子：
可以發現到，當|𝑊𝑖
𝑡
𝑡
| ≤ η 𝑡+
1
2
λ 時，會對𝑊𝑖
𝑡+1
進行截斷(Truncating)
換句話說：
𝑊𝑖
𝑡+1
= sgn 𝑊𝑖
𝑡
𝑡
max(0, |𝑊𝑖
𝑡
𝑡
| − η 𝑡+
1
2
λ )
𝑊𝑡+1
=
0 , 𝑖𝑓 𝑊𝑖
𝑡
𝑡
≤ η
𝑡+
1
2
λ
𝑊𝑖
𝑡
𝑡
− sgn 𝑊𝑖
𝑡
𝑡
η
𝑡+
1
2
λ , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(可以這麼解釋)
當一個新樣本產生的梯度，不足以讓該維度權重產生足夠大的變化時，
認為該維度在本次更新中不重要，因此令其權重為 0

4. TG vs FOBOS
27
• L1-FOBOS
𝑊𝑡+1
=
0 , 𝑖𝑓 𝑊𝑖
𝑡
𝑡
≤ η
𝑡+
1
2
λ
𝑊𝑖
𝑡
𝑡
− sgn 𝑊𝑖
𝑡
𝑡
η
𝑡+
1
2
λ , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
有趣的是，當 K=1,
θ = INF, TG = L1-FOBOS
α = η 𝑡+
1
2
λ
• TG
𝑊𝑡+1 =
𝜕𝑊𝑡
, η𝒈𝒊, θ) , 𝑖𝑓 𝑚𝑜𝑑 𝑡, 𝐾 = 0
𝑊𝑡 − η
𝜕𝑊𝑡
, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑇1(𝑣𝑖, α, θ) =
0 , 𝑖𝑓 |𝑣𝑖| ≤ α
𝑣𝑖 −α ∗ 𝑠𝑔𝑛 𝑣𝑖 , 𝑖𝑓α ≤ | 𝑣𝑖 | ≤ θ

0. Agenda
 Loss Function
 Regularization
28

Reference
[1] John Langford, Lihong Li & Tong Zhang. Sparse Online Learning via
Truncated Gradient. Journal of Machine Learning Research, 2009.
[2] John Duchi & Yoram Singer. Efficient Online and Batch Learning using
Forward Backward Splitting. Journal of Machine Learning Research, 2009.
[3] Lin Xiao. Dual Averaging Methods for Regularized Stochastic Learning
and Online Optimization. Journal of Machine Learning Research, 2010.
[4] H. B. McMahan. Follow-the-regularized-leader and mirror descent:
Equivalence theorems and L1 regularization. In AISTATS, 2011.
[5] H. Brendan McMahan,Gary Holt, D. Sculley et al. Ad Click Prediction: a
View from the Trenches. In KDD , 2013.
29

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