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Found 55 papers, 37 papers with code
Exponentially Weighted Moving Models
We propose a general method for computing an approximation of EWMM, which requires storing only a window of a fixed number of past samples, and uses an additional quadratic term to approximate the loss associated with the data before the window.
Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization
We propose a new method for finding statistical arbitrages that can contain more assets than just the traditional pair.
Markowitz Portfolio Construction at Seventy
More than seventy years ago Harry Markowitz formulated portfolio construction as an optimization problem that trades off expected return and risk, defined as the standard deviation of the portfolio returns.
Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices
The second is rank allocation, where we choose the ranks of the blocks in each level, subject to the total rank having a given value, which preserves the total storage needed for the MLR matrix.
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
We consider robust empirical risk minimization (ERM), where model parameters are chosen to minimize the worst-case empirical loss when each data point varies over a given convex uncertainty set.
A Simple Method for Predicting Covariance Matrices of Financial Returns
We also test covariance predictors on downstream applications such as portfolio optimization methods that depend on the covariance matrix.
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models
Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called strata (e. g., age, region, time, forecast horizon, etc.
A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer
We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives using a target-priority-limit scalarizer.
Portfolio Construction as Linearly Constrained Separable Optimization
Mean-variance portfolio optimization problems often involve separable nonconvex terms, including penalties on capital gains, integer share constraints, and minimum position and trade sizes.
Portfolio Optimization
Optimization and Control
Portfolio Management
Minimum-Distortion Embedding
Our software scales to data sets with millions of items and tens of millions of distortion functions.
Portfolio Performance Attribution via Shapley Value
We consider an investment process that includes a number of features, each of which can be active or inactive.
Covariance Prediction via Convex Optimization
We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector.
Low Rank Forecasting
Our focus is on low rank forecasters, which break forecasting up into two steps: estimating a vector that can be interpreted as a latent state, given the past, and then estimating the future values of the time series, given the latent state estimate.
Portfolio Construction Using Stratified Models
In this paper we develop models of asset return mean and covariance that depend on some observable market conditions, and use these to construct a trading policy that depends on these conditions, and the current portfolio holdings.
Sample Efficient Reinforcement Learning with REINFORCE
Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory.
Learning Convex Optimization Models
A convex optimization model predicts an output from an input by solving a convex optimization problem.
Multi-Period Liability Clearing via Convex Optimal Control
We consider the problem of determining a sequence of payments among a set of entities that clear (if possible) the liabilities among them.
Optimal Representative Sample Weighting
We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages of the data are close to prescribed values.
Fitting Laplacian Regularized Stratified Gaussian Models
We consider the problem of jointly estimating multiple related zero-mean Gaussian distributions from data.
Differentiating through Log-Log Convex Programs
We use the adjoint of the derivative to implement differentiable log-log convex optimization layers in PyTorch and TensorFlow.
Optimization and Control
Convex Optimization Over Risk-Neutral Probabilities
We describe a number of convex optimization problems over the convex set of risk neutral price probabilities.
Fundamental bounds for scattering from absorptionless electromagnetic structures
We illustrate our bounding procedure by studying limits on the scattering cross-sections of dielectric and metallic particles in the absence of material losses.
Optics
A New Heuristic for Physical Design
In a physical design problem, the designer chooses values of some physical parameters, within limits, to optimize the resulting field.
Optimization and Control Computational Physics Optics
Automatic Repair of Convex Optimization Problems
Given an infeasible, unbounded, or pathological convex optimization problem, a natural question to ask is: what is the smallest change we can make to the problem's parameters such that the problem becomes solvable?
Optimization and Control
Eigen-Stratified Models
This leads to a reduction, sometimes large, of model size when $m \leq n$ and $m \ll K$.
Learning Convex Optimization Control Policies
Common examples of such convex optimization control policies (COCPs) include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex control-Lyapunov or approximate dynamic programming (ADP) policies.
Differentiable Convex Optimization Layers
In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization.
Minimizing a Sum of Clipped Convex Functions
We consider the problem of minimizing a sum of clipped convex functions; applications include clipped empirical risk minimization and clipped control.
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize
Under this metric selection for VM-PG, the theoretical convergence is analyzed.
A General Optimization Framework for Dynamic Time Warping
We pose the choice of warping function as an optimization problem with several terms in the objective.
Disciplined Quasiconvex Programming
We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions.
Optimization and Control Mathematical Software
A Distributed Method for Fitting Laplacian Regularized Stratified Models
In a basic and traditional formulation a separate model is fit for each value of the categorical feature, using only the data that has the specific categorical value.
Differentiating Through a Cone Program
These correspond to computing an approximate new solution, given a perturbation to the cone program coefficients (i. e., perturbation analysis), and to computing the gradient of a function of the solution with respect to the coefficients.
Optimization and Control
Least Squares Auto-Tuning
Least squares is by far the simplest and most commonly applied computational method in many fields.
Computational Bounds For Photonic Design
Physical design problems, such as photonic inverse design, are typically solved using local optimization methods.
Optics Optimization and Control Computational Physics
Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Models for predicting aircraft motion are an important component of modern aeronautical systems.
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
In this paper, we examine a class of non-convex stochastic optimization problems which we call variationally coherent, and which properly includes pseudo-/quasiconvex and star-convex optimization problems.
OSQP: An Operator Splitting Solver for Quadratic Programs
We present a general purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.
Optimization and Control
A Rewriting System for Convex Optimization Problems
We describe a modular rewriting system for translating optimization problems written in a domain-specific language to forms compatible with low-level solver interfaces.
Optimization and Control Mathematical Software
On the convergence of mirror descent beyond stochastic convex programming
In this paper, we examine the convergence of mirror descent in a class of stochastic optimization problems that are not necessarily convex (or even quasi-convex), and which we call variationally coherent.
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively.
Multi-Period Trading via Convex Optimization
The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made.
Network Inference via the Time-Varying Graphical Lasso
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements.
Dirty Pixels: Towards End-to-End Image Processing and Perception
As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping and compression.
Greedy Gaussian Segmentation of Multivariate Time Series
We consider the problem of breaking a multivariate (vector) time series into segments over which the data is well explained as independent samples from a Gaussian distribution.
Optimization and Control
Saturating Splines and Feature Selection
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range.
Disciplined Multi-Convex Programming
A multi-convex optimization problem is one in which the variables can be partitioned into sets over which the problem is convex when the other variables are fixed.
Optimization and Control
Convolutional Imputation of Matrix Networks
A matrix network is a family of matrices, with relatedness modeled by a weighted graph.
Convex Optimization With Abstract Linear Operators
We introduce a convex optimization modeling framework that transforms a convex optimization problem expressed in a form natural and convenient for the user into an equivalent cone program in a way that preserves fast linear transforms in the original problem.
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method.
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights
We derive a second-order ordinary differential equation (ODE), which is the limit of Nesterov’s accelerated gradient method.
Convex Optimization in Julia
This paper describes Convex, a convex optimization modeling framework in Julia.
Generalized Low Rank Models
Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.
Accuracy at the Top
We introduce a new notion of classification accuracy based on the top $\tau$-quantile values of a scoring function, a relevant criterion in a number of problems arising for search engines.
Distributed Large Scale Network Utility Maximization
Using an empirical evaluation we show that our new method outperforms previous approaches, including the truncated Newton method and dual-decomposition methods.
Information Theory Distributed, Parallel, and Cluster Computing Information Theory Optimization and Control
