Bin packing problems?

Can the bin packing problem be solved using integer programming?

The problem of material requirements planning can be formalized as a bin-packing problem, which can be solved using a mixed-integer linear program (MILP).

What is automatic bin packing?

Automatic bin packing: Kubernetes places containers automatically based on the required resources and other restrictions without impairing availability. Automated rollouts and rollbacks: Kubernetes distributes changes to the software or the configuration using a rollout.

Is bin packing NP-complete?

Bin Packing belongs to NP and thus NP-Complete:

In order to consider a problem to be NP, it should be able to verify the problem in polynomial time[1].

What is vector bin packing?

Abstract. We consider a variant of bin packing called multiple-choice vector bin packing. In this problem, we are given a set of items, where each item can be selected in one of several -dimensional incarnations. We are also given bin types, each with its own cost and -dimensional size.

What is bin packing in Kubernetes?

Automatic bin packing: You provide Kubernetes with a cluster of nodes that it can use to run containerized tasks. You tell Kubernetes how much CPU and memory (RAM) each container needs. Kubernetes can fit containers onto your nodes to make the best use of your resources.

What is multiple knapsack?

The multiple knapsack problem is a generalization of the standard knapsack problem (KP) from a single knapsack to m knapsacks with (possibly) different capacities.

What makes an algorithm greedy?

A greedy algorithm is an algorithmic strategy that makes the best optimal choice at each small stage with the goal of this eventually leading to a globally optimum solution. This means that the algorithm picks the best solution at the moment without regard for consequences.

What does it mean that the next fit approximation for online bin packing problem is 2 approximate?

Next Fit is 2 approximate, i.e., the number of bins used by this algorithm is bounded by twice of optimal. Consider any two adjacent bins. The sum of items in these two bins must be > c; otherwise, NextFit would have put all the items of second bin into the first.

What are NP hard problems?

A problem is NP-hard if an algorithm for solving it can be translated into one for solving any NP- problem (nondeterministic polynomial time) problem. NP-hard therefore means "at least as hard as any NP-problem," although it might, in fact, be harder.

What is cluster Autoscaler?

Cluster Autoscaler is a tool that automatically adjusts the size of the Kubernetes cluster based on the utilization of Pods and Nodes in your cluster. For more general information about the Cluster Autoscaler, please see the project documentation.

What is self healing in Kubernetes?

Self-healing is a feature provided by the Kubernetes open-source system. If a containerized app or an application component fails or goes down, Kubernetes re-deploys it to retain the desired state. Kubernetes provides self-healing by default.

Is factorization NP-complete?

Integer-factorization is considered NP, but not NP-complete since no reduction proof is known.

Do programmers memorize algorithms?

In fact, most programming jobs don't require the memorization of approach, rather they are interested in your way of recognizing algorithmic pattern when faced with the problem. As a matter of fact, there is abundance of information in most programming blogs/articles on algorithm topics.

Is Kruskal algorithm greedy?

It is a greedy algorithm in graph theory as in each step it adds the next lowest-weight edge that will not form a cycle to the minimum spanning forest.

What kind of problems can be solved using greedy algorithm?

What is simple knapsack problem?

The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.

What are the two types of knapsack problem?

Nested knapsack problem. Collapsing knapsack problem. Nonlinear knapsack problem. Inverse-parametric knapsack problem.

What is greedy knapsack problem?

The basic idea of the greedy approach is to calculate the ratio value/weight for each item and sort the item on basis of this ratio. Then take the item with the highest ratio and add them until we can't add the next item as a whole and at the end add the next item as much as we can.