soco 1.0.1

Algorithms for Smoothed Online Convex Optimization
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---
layout: primer_without_heading
title: Algorithms - Algorithms for Smoothed Online Convex Optimization
---

# Problem Variants

* **Smoothed Convex Optimization (SCO)**
* **Simplified Smoothed Convex Optimization (SSCO)** - decision space is lower bounded by `0`, movement costs are a dimension-dependently scaled Manhattan distance
* **Smoothed Balanced-Load Optimization (SBLO)** - SSCO and hitting costs are computed by _balancing_ incoming loads across all dimensions each of which is described by a convex cost function
* **Smoothed Load Optimization (SLO)** - SSCO and hitting costs are time independent and linear in some incoming load

# Algorithms

## Offline

### Backward-Recurrent Capacity Provisioning [1]

SSCO - Fractional - Uni-Dimensional

Stays within bounds on the optimal solution moving backwards in time.

### Convex Optimization

SCO - Fractional

Finds the minimizer of a convex objective.

### Graph-Based Optimal Algorithm (in one dimension) [5]

SSCO - Integral - Uni-Dimensional

Finds a shortest path in a graph using dynamic programming (in polynomial time).

### Graph-Based Optimal Algorithm [9]

SSCO - Integral

Finds a shortest path in a graph using dynamic programming (**not** in polynomial time).

### Graph-Based Polynomial-Time Approximation Scheme [9]

SSCO - Integral

Extends the graph-based optimal algorithm by only considering a subset of the decision space to achieve a better performance.

### Static Fractional Optimum

SCO - Fractional

Solves a smaller problem than _Convex Optimization_ to obtain an optimal static solution.

### Static Integral Optimum

SCO - Integral

Cycles through _all_ possible configurations to find the optimal static integral solution. Convexity is used to stop the search of the decision space quickly in practice, however, the worst-case runtime is exponential.

## Online

### Lazy Capacity Provisioning [1]

SSCO - Fractional - Uni-Dimensional - `3`-competitive

Lazily stays within fractional bounds on the decision space.

### Lazy Capacity Provisioning [5]

SSCO - Integral - Uni-Dimensional - `3`-competitive (optimal for a deterministic algorithm)

Lazily stays within integral bounds on the decision space.

### Memoryless algorithm [3]

SSCO - Fractional - Uni-Dimensional - `3`-competitive (optimal for a memoryless algorithm)

Moves towards the minimizer of the hitting cost balancing the paid movement cost. Special case of Primal Online Balanced Descent.

### Probabilistic Algorithm [3]

SSCO - Fractional - Uni-Dimensional - `2`-competitive (optimal)

Constructs a probability distribution of well-performing configurations over time.

### Randmoly Biased Greedy [4]

SCO - Fractional - Uni-Dimensional - `2`-competitive (optimal)

Uses a work function to balance hitting and movement costs.

### Randomized Integral Relaxation [5]

SSCO - Integral - Uni-Dimensional - `2`-competitive (optimal)

Randomly rounds solutions of any `2`-competitive fractional algorithm.

### Lazy Budgeting for SLO [8]

SLO - Integral - `2d`-competitive (optimal for a deterministic algorithm)

Keeps servers active for some minimum time delta even when they are not use to balance hitting costs and movement costs.

### Lazy Budgeting for SLO (randomized) [8]

SLO - Integral - `(e / (e - 1))d`-competitive

Keeps servers active for some minimum time delta even when they are not use to balance hitting costs and movement costs.

### Lazy Budgeting for SBLO [9]

SBLO - Integral - `2d + 1 + \epsilon`-competitive

Keeps servers active for some minimum time delta even when they are not use to balance hitting costs and movement costs.

### Online Gradient Descent [4]

SCO - Fractional

Achieves sublinear regret by _learning_ the static offline optimum.

### Primal Online Balanced Descent [6]

SCO - Fractional - `3+\mathcal{O}(1/\alpha)`-competitive for the `\ell_2` norm and locally `\alpha`-polyhedral hitting costs, `\mathcal{O}(\sqrt{d})`-competitive for the `\ell_1` norm; mirror map must be `m`-strongly convex and `M`-Lipschitz smooth in the switching cost norm

Takes a gradient step orthogonal to some landing level set balancing costs in the primal space.

### Dual Online Balanced Descent [6]

SCO - Fractional - mirror map must be `m`-strongly convex and `M`-Lipschitz smooth in the switching cost norm

Takes a gradient step orthogonal to some landing level set balancing costs in the dual space. Achieves sublinear regret.

### Greedy Online Balanced Descent [7]

SCO - Fractional - `\mathcal{O}(1 / \sqrt{m})`-competitive for `m`-quasiconvex hitting costs and `\ell_2`-squared switching costs

Takes a normal OBD-step and then an additional step directly towards the minimizer of the hitting cost depending on the convexity parameter `m`.

### Regularized Online Balanced Descent [7]

SCO - Fractional - `\mathcal{O}(1 / \sqrt{m})`-competitive (optimal) for `m`-strongly convex and differentiable hitting costs and switching costs modeled as the Bregman divergence where the potential function is `\alpha`-strongly convex, `\beta`-strongly smooth, differentiable, and its Fenchel Conjugate is differentiable; `\Omega(1/m)`-competitive for `m`-quasiconvex hitting costs and `\ell_2`-squared switching costs

Using a computationally simpler local view.

### Receding Horizon Control [2]

SSCO - Fractional - `(1 + \Omega(\beta/e_0))`-competitive where `e_0` is the idle cost and `\beta` is the scaling of the Manhattan distance; when uni-dimensional the competitive ratio is `1 + \mathcal{O}(1/w)`

### Averaging Fixed Horizon Control [2]

SSCO - Fractional - `1 + \mathcal{O}(1/w)`-competitive

# References

1. Minghong Lin and Adam Wierman and Lachlan L. H. Andrew and Eno Thereska. _Dynamic right-sizing for power-proportional data centers_. 2011.
2. Minghong Lin and Zhenhua Liu and Adam Wierman and Lachlan L. H. Andrew. _Online Algorithms for Geographical Load Balancing_. 2012.
3. Nikhil Bansal and Anupam Gupta and Ravishankar Krishnaswamy and Kirk Pruhs and Kevin Schewior and Cliff Stein. _A 2-Competitive Algorithm For Online Convex Optimization With Switching Costs_. 2015.
4. Lachlan L. H. Andrew and Siddharth Barman and Katrina Ligett and Minghong Lin and Adam Myerson and Alan Roytman and Adam Wierman. _A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret_. 2015.
5. Susanne Albers and Jens Quedenfeld. _Optimal Algorithms for Right-Sizing Data Centers_. 2018.
6. Niangjun Chen and Gautam Goel and Adam Wierman. _Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent_. 2018.
7. Gautam Goel and Yiheng Lin and Haoyuan Sun and Adam Wierman. _Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization_. 2019.
8. Susanne Albers and Jens Quedenfeld. _Algorithms for Energy Conservation in Heterogeneous Data Centers_. 2021.
9. Susanne Albers and Jens Quedenfeld. _Algorithms for Right-Sizing Heterogeneous Data Centers_. 2021.