Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Convenience Sampling Method

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...

Polar Equations of Conics

Polar Equations of Conics

A conic section can be defined in polar coordinates as the set of all points whose distance from a fixed point, known as the focus, bears a constant ratio to their distance from a fixed line, known as the directrix. This constant ratio is called the eccentricity. This definition unifies all types of conic sections—ellipses, parabolas, and hyperbolas—under a single framework. When the focus is positioned at the origin of the polar coordinate system, a single polar equation can describe any conic...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Optimization Problems

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

Application of Nonlinear Inequalities

Application of Nonlinear Inequalities

A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality: can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the key values are 3...

Random Sampling Method

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity.

mAbs·2025

Same author

Optimally selecting the top <i>k</i> values from <i>X</i> + <i>Y</i> with layer-ordered heaps.

PeerJ. Computer science·2021

Same author

Selection on <i>X</i> <sub>1</sub> + <i>X</i> <sub>2</sub> + ⋯ + <i>X</i> <sub></sub> via Cartesian product trees.

PeerJ. Computer science·2021

Same author

Performing Selection on a Monotonic Function in Lieu of Sorting Using Layer-Ordered Heaps.

Journal of proteome research·2021

Same author

Fast Exact Computation of the <i>k</i> Most Abundant Isotope Peaks with Layer-Ordered Heaps.

Analytical chemistry·2020

Same author

EPIFANY: A Method for Efficient High-Confidence Protein Inference.

Journal of proteome research·2020

Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026

Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026

Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026

Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026

Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026

Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026

See all related articles

Search research articles

Related Experiment Videos

Conic sampling: an efficient method for solving linear and quadratic programming by randomly linking constraints

Oliver Serang¹

¹Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, United States of America. Oliver.Serang@Childrens.Harvard.edu

|September 7, 2012

Summary

This summary is machine-generated.

A new randomized conic sampling method offers a competitive alternative for solving linear programming (LP) problems and quadratic programs (QP). This approach shows favorable runtime performance compared to existing algorithms, particularly for specific problem types.

Related Experiment Videos

Area of Science:

Optimization
Computational Mathematics
Operations Research

Background:

Linear programming (LP) is crucial for analysis and resource allocation, often approximating complex problems.
Traditional LP methods lack diversity, with randomized algorithms underutilized in practice.
Existing LP solvers face challenges with certain polytope characteristics.

Purpose of the Study:

Introduce a novel randomized algorithm for solving linear programming (LP) problems.
Evaluate the performance of this new method against established LP algorithms.
Adapt the method for solving quadratic programming (QP) problems, specifically projections onto polytopes.

Main Methods:

Developed a conic sampling method involving movement along polytope facets and interior using randomly sampled rays.
Applied the conic sampling method to randomly generated LP instances.
Adapted the conic sampling method to solve a specific type of quadratic program (QP).

Main Results:

The conic sampling method demonstrated competitive runtime performance compared to simplex and primal affine-scaling algorithms for LPs.
Performance advantages were noted particularly on polytopes with specific geometric characteristics.
The adapted method outperformed proprietary software (Mathematica) on large, sparse QP problems derived from mass spectrometry proteomics.

Conclusions:

The novel conic sampling method presents a viable and efficient randomized approach for LP.
This technique shows promise for tackling complex optimization problems, including certain QPs.
The method's effectiveness on large, sparse QP problems has significant implications for fields like proteomics.