Questions tagged [online-algorithms]

Questions about algorithms that receive the input piecewise and have to make decisions, that is produce output, before having seen the whole input.

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On the limitation of the “adversary” in randomization for online learning

While reading on randomization for online learning from the text "Online Learning and Online Convex Optimization" by Shai Shalev-Shwartz, I found the following statement (Pg 115)- "The adversary can ...
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62 views

Multi Arm Bandit (MAB) — Increasing reward function

In the general stochastic MAB model, the reward obtained at each trial is generally assumed to be independent of previous trials and obtained from some fixed (but unknown) distribution. However, if ...
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How to define the potential function method for an online maximization problem?

From Online Computation and Competitive Analysis By Allan Borodin, Ran El-Yaniv, to prove that an online algorithm $\text{ALG}$ is $c$-competitive for a minimization problem (i.e., there exists a ...
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Update model parameter with new data, discarding old data

I have this dataset, and I am using y = (a * x^n) / (b + x^n) Hill function as the model, where a is the limit of the Hill curve,...
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102 views

Competitive ratio of this paging algorithm

In the paging problem, we have a cache of size $k$ and a universe of $n>k$ pages. In an online setting, we get requests for pages $p_1,p_2,\ldots p_t$, and are required to have page $p_i$ loaded ...
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Winnow versus Perceptron - Why adding irrelevant features increases L2(X) but not L∞(X)?

I saw here: http://www.cs.cmu.edu/~ninamf/ML11/lect0906.pdf Intuitively, if “n” is large but most features are irrelevant (i.e. target is sparse but examples are dense), then Winnow is better because ...
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1answer
183 views

Can map-reduce speed up the count-min-sketch algorithm?

Is there any possibility of improvement in the result of count-min-sketch algorithm if we will use Map Reduce approach? Improvement in performance can be in terms of accuracy, time complexity or the ...
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569 views

Streaming Knapsack Problem

I want to implement efficiently "streaming Knapsack" problem in java. The problem is I have a stream input of integer data coming continuously for example -1, 2, 9, 5, 5, 11, 1 -3,... The question ...
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55 views

Online shortest path in ordered DAG

Suppose I have an edge-weighted connected rooted DAG $G = (V, E, r \in V, w \in E \to \mathbb{Z})$ where there exists a sequence of nonempty sets (called "levels") $L_0 = \{r\}, L_1 \subset ...
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32 views

How to simulate online matching algorithms (implementation)

I was reading about online algorithms and bipartite matching. I found an implementation that works fine on several websites (like geeksforgeeks). For the online version, I found this paper https://...
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Online Set Filling

Suppose we have 3 sets $A, B, C$ which can can hold a maximum of two elements, each. So the total number of elements that the sets can hold together i.e. total capacity (TC) is $ 3*2 = 6$. Now, ...
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47 views

Which of the following statement is true?

Given two statement: A:if inputs of a problem is online, then there is faster or equal algorithm in view of time complexity for that problem when input is offline. B:if inputs of a problem is offline, ...
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What is an optimal online algorithm and how to design it to solve NP-hard problems?

To solve an NP-hard optimization problem optimally, we can use mathematical programming or brute force techniques. I would like to know if it is possible to solve the problem in an online fashion ...
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Does regret change when the loss function is dependent on the previous predictions?

The loss function of each expert in the expert advice problem(or any online learning problem) depends on the time($t$) and expert advice at that time($f_{t}(i)$). suppose in this problem, loss ...
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45 views

Scheduling jobs online on 3 identical machines - a lower bound of 5/3

Consider the Online Scheduling Problem with $3$ identical machines. Jobs, with arbitrary size arrive online one after another and need to be scheduled immediately on one of the $3$ machines without ...
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Optimizing convex function in an online manner

I have a convex function of $n$ variables, $f(x_1,x_2,\dots,x_n)$ and need to find its minimizer. Are there algorithms that can retrieve the minimizer in an online fashion? i.e. solve for $x_1^{(opt)}$...

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